22.3.6.1.11 Face Action Units for Expressions and Motion Analysis, FAU, FACS

Huang, C.L., Huang, Y.M.,
Facial Expression Recognition Using Model-Based Feature Extraction and Action Parameter(s) Classification,
JVCIR(8), 1997, pp. 278-290. BibRef 9700

Essa, I.A.[Irfan A.], and Pentland, A.P.[Alex P.],
Coding, Analysis, Interpretation, and Recognition of Facial Expressions,
PAMI(19), No. 7, July 1997, pp. 757-763.
IEEE DOI 9708
BibRef
Earlier:
Facial Expression Recognition Using Image Motion,
MBR97(Chapter 12). MIT. BibRef
Earlier:
Facial Expression Recognition Using a Dynamic Model and Motion Energy,
ICCV95(360-367).
IEEE DOI BibRef
And: Vismod-307, 1995.
HTML Version. and
PS File. Spatio-temporal technique for expression recognition. Rather than use the heuristic (psychological system) FACS, use a probabilistic scheme to characterize motion and muscle activation. BibRef

Essa, I.A., Pentland, A.P.,
A Vision System for Observing and Extracting Facial Action Parameters,
CVPR94(76-83).
IEEE DOI BibRef 9400
And: Vismod-247, 1993.
HTML Version. and
PS File. BibRef

Essa, I.A.[Irfan A.], Darrell, T.J.[Trevor J.], Pentland, A.P.[Alex P.],
Tracking Facial Motion,
Vismod-272, 1994.
HTML Version. and
PS File. BibRef 9400

Essa, I.A.[Irfan A.],
Analysis, Interpretation and Synthesis of Facial Expressions,
Vismod-303, 1994.
HTML Version. and
PS File. BibRef 9400

Zhang, L.[Liang],
Automatic Adaptation of a Face Model Using Action Units for Semantic Coding of Videophone Sequences,
CirSysVideo(8), No. 6, October 1998, pp. 781.
IEEE Top Reference. BibRef 9810
Earlier:
Estimation of the Mouth Features Using Deformable Templates,
ICIP97(III: 328-331).
IEEE DOI 9710
Lipreading. BibRef

Donato, G.[Gianluca], Bartlett, M.S.[Marian Stewart], Hager, J.C.[Joseph C.], Ekman, P.[Paul], Sejnowski, T.J.[Terrence J.],
Classifying Facial Actions,
PAMI(21), No. 10, October 1999, pp. 974-989.
IEEE DOI Uses the FACS (
See also Facial Action Coding System: A Technique for the Measurement of Facial Movement, The. ) coding system. 96% for 12 facial actions. BibRef 9910

Bartlett, M.S., Hager, J.C., Ekman, P., Sejnowski, T.J.,
Measuring Facial Expressions by Computer Image Analysis,
Psychophysiology(36), 1999, pp. 253-264. BibRef 9900

Fleet, D.J.[David J.], Black, M.J.[Michael J.], Yacoob, Y.[Yaser], Jepson, A.D.[Allan D.],
Design and Use of Linear Models for Image Motion Analysis,
IJCV(36), No. 3, February-March 2000, pp. 171-193.
DOI Link 0003

PDF File.
See also Probabilistic Detection and Tracking of Motion Boundaries.
See also Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion.
See also Estimating Optical-Flow in Segmented Images Using Variable-Order Parametric Models with Local Deformations. BibRef

Black, M.J.[Michael J.],
Explaining Optical Flow Events with Parameterized Spatio-temporal Models,
CVPR99(I: 326-332).
IEEE DOI Generative Spatio-Temporal model. Basis for flow fields. Used for facial expressions. BibRef 9900

Black, M.J., Yacoob, Y., Jepson, A.D., Fleet, D.J.,
Learning Parameterized Models of Image Motion,
CVPR97(561-567).
IEEE DOI 9704
Several applications. BibRef

Tian, Y.L.[Ying-Li], Kanade, T.[Takeo], Cohn, J.F.[Jeffrey F.],
Recognizing Action Units for Facial Expression Analysis,
PAMI(23), No. 2, February 2001, pp. 97-115.
IEEE DOI 0102
BibRef
And: CMU-RI-TR-99-40, December, 1999.
HTML Version. BibRef
And:
Recognizing Upper Face Action Units for Facial Expression Analysis,
CVPR00(I: 294-301).
IEEE DOI 0005
Analyze expressions using permanent facial features and transient features. Describe expressions in terms of action units rather than prototypical expressions. Compares results with:
See also Measuring Facial Expressions by Computer Image Analysis.
See also Classifying Facial Actions.
See also Automated Face Analysis by Feature Point Tracking Has High Concurrent Validity with Manual FACS Coding. and
See also Detection, Tracking, and Classification of Action Units in Facial Expression. BibRef

Tian, Y.L., Kanade, T., Cohn, J.F.,
Recognizing Facial Actions by Combining Geometric Features and Regional Appearance Patterns,
CMU-RI-TR-01-01, January 2001.
PDF File. 0102
BibRef

Tian, Y.L.[Ying-Li], Kanade, T.[Takeo], Cohn, J.F.[Jeffrey F.],
Evaluation of gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity,
AFGR02(218-223).
IEEE DOI 0206
BibRef
Earlier:
Recognizing Lower Face Action Units in Facial Expression,
AFGR00(484-490).
IEEE DOI 0003
BibRef
And:
Multi-State Based Facial Feature Tracking and Detection,
CMU-RI-TR-99-18, August, 1999.
HTML Version. BibRef

Cohn, J.F.[Jeffrey F.], Kanade, T.[Takeo],
Use of automated facial image analysis for measurement of emotion expression,
EmotionElicitation06(xx-yy).
HTML Version. BibRef 0600

Cohn, J.F.[Jeffrey F.], Ambadar, Z., and Ekman, P.,
Observer-based measurement of facial expression with the Facial Action Coding System,
EmotionElicitation06(xx-yy).
HTML Version. BibRef 0600

Wang, T.H.[Te-Hsun], Lien, J.J.J.[Jenn-Jier James],
Facial expression recognition system based on rigid and non-rigid motion separation and 3D pose estimation,
PR(42), No. 5, May 2009, pp. 962-977.
Elsevier DOI 0902
Facial expression recognition; FACS action units; Rigid and non-rigid motion separation; 3D pose estimation; Perspective projection
See also view-based statistical system for multi-view face detection and pose estimation, A. BibRef

Lai, T.H.[Tzung-Heng], Wang, T.H.[Te-Hsun], Lien, J.J.J.[Jenn-Jier James],
Incremental Perspective Motion Model for Rigid and Non-rigid Motion Separation,
PSIVT07(613-624).
Springer DOI 0712
Motion extraction in facial expression analysis. BibRef

Lien, J.J.J.[James J.J.], Kanade, T.[Takeo], Cohn, J.F.[Jeffrey F.], Li, C.C.[Ching-Chung],
Automated Facial Expression Recognition Based on FACS Action Units,
AFGR98(390-395).
IEEE DOI BibRef 9800

Lien, J.J.J.,
Automatic Recognition of Facial Expressions Using Hidden Markov Models and Estimation of Expression Intensity,
CMU-RI-TR-98-31, April, 1998.
HTML Version. BibRef 9804

Lien, J.J.J.[James J.J.], Kanade, T.[Takeo], Cohn, J.F.[Jeffrey F.], Li, C.C.[Ching-Chung], Zlochower, A.J.[Adena J.],
Subtly Different Facial Expression Recognition and Expression Intensity Estimation,
CVPR98(853-859).
IEEE DOI
PDF File. BibRef 9800

Cohn, J.F., Zlochower, A.J., Lien, J.J.J., Kanade, T.,
Automated Face Analysis by Feature Point Tracking Has High Concurrent Validity with Manual FACS Coding,
Psychophysiology(36), 1999, pp. 35-43.
PDF File. BibRef 9900
Earlier:
Feature-Point Tracking by Optical Flow Discriminates Subtle Differences in Facial Expression,
AFGR98(396-401).
IEEE DOI BibRef

CMU Facial Expression Database,
1999 Dataset, Faces. Dataset, Facial Expression.
HTML Version. Includes annotation.

Lien, J.J.J.[James J.J.], Kanade, T.[Takeo], Cohn, J.F.[Jeffrey F.], Li, C.C.[Ching-Chung],
Detection, Tracking, and Classification of Action Units in Facial Expression,
RAS(31), 2000, pp. 131-146. BibRef 0001

Tao, H.[Hai], Huang, T.S.[Thomas S.],
Visual Estimation and Compression of Facial Motion Parameters: Elements of a 3D Model-Based Video Coding System,
IJCV(50), No. 2, November 2002, pp. 111-125.
DOI Link 0210
BibRef
Earlier:
Explanation-based Facial Motion Tracking Using a Piecewise Bezier Volume Deformation Model,
CVPR99(I: 611-617).
IEEE DOI BibRef

Tao, H.[Hai], Huang, T.S.[Thomas S.],
A Piecewise Bézier Volume Deformation Model and Its Applications in Facial Motion Capture,
AIPU02(39-56). 0905
BibRef

Tao, H.[Hai], Lopez, R., Huang, T.S.,
Tracking Facial Features Using Probabilistic Network,
AFGR98(166-170).
IEEE DOI BibRef 9800

Pardàs, M.[Montse], Bonafonte, A.[Antonio],
Facial animation parameters extraction and expression recognition using Hidden Markov Models,
SP:IC(17), No. 9, October 2002, pp. 675-688.
Elsevier DOI 0211
BibRef

Chuang, C.F.[Chao-Fa], Shih, F.Y.[Frank Y.],
Recognizing facial action units using independent component analysis and support vector machine,
PR(39), No. 9, September 2006, pp. 1795-1798.
Elsevier DOI 0606
Action unit; Independent component analysis; Support vector machine BibRef

Buciu, I.[Ioan], Pitas, I.[Ioannis],
NMF, LNMF, and DNMF modeling of neural receptive fields involved in human facial expression perception,
JVCIR(17), No. 5, October 2006, pp. 958-969.
Elsevier DOI 0711
BibRef
Earlier: Erratum: JVCIR(18), No. 1, February 2007, pp. 100.
Elsevier DOI 0711
BibRef
Earlier:
Application of non-negative and local non negative matrix factorization to facial expression recognition,
ICPR04(I: 288-291).
IEEE DOI 0409
Image representation; Receptive fields; Facial expressions BibRef

Nikitidis, S.[Symeon], Tefas, A.[Anastasios], Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
Subclass discriminant Nonnegative Matrix Factorization for facial image analysis,
PR(45), No. 12, December 2012, pp. 4080-4091.
Elsevier DOI 1208
BibRef
Earlier:
Facial expression recognition using clustering discriminant Non-negative Matrix Factorization,
ICIP11(3001-3004).
IEEE DOI 1201
Nonnegative Matrix Factorization; Subclass discriminant analysis; Multiplicative updates; Facial expression recognition; Face recognition BibRef

Orfanidis, G.[Georgios], Tefas, A.[Anastasios], Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
Facial image clustering in stereoscopic videos using double spectral analysis,
SP:IC(33), No. 1, 2015, pp. 86-105.
Elsevier DOI 1504
Stereoscopic video BibRef

Nikitidis, S.[Symeon], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Maximum Margin Projection Subspace Learning for Visual Data Analysis,
IP(23), No. 10, October 2014, pp. 4413-4425.
IEEE DOI 1410
face recognition BibRef

Kotsia, I.[Irene], Pitas, I.[Ioannis],
Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines,
IP(16), No. 1, January 2007, pp. 172-187.
IEEE DOI 0701
BibRef
Earlier:
Real Time Facial Expression Recognition from Image Sequences Using Support Vector Machines,
ICIP05(II: 966-969).
IEEE DOI 0512
BibRef

Kotsia, I.[Irene], Zafeiriou, S.P.[Stefanos P.], Pitas, I.[Ioannis],
Texture and shape information fusion for facial expression and facial action unit recognition,
PR(41), No. 3, March 2008, pp. 833-851.
Elsevier DOI 0711
BibRef
Earlier:
Fusion of Geometrical and Texture Information for Facial Expression Recognition,
ICIP06(2649-2652).
IEEE DOI 0610
Facial expression recognition; Facial Action Unit recognition; Discriminant Non-Negative Matrix Factorization; Multidimensional embedding; Support Vector Machines; Radial Basis Functions; Fusion BibRef

Kotsia, I.[Irene], Buciu, I.[Ioan], Pitas, I.[Ioannis],
An analysis of facial expression recognition under partial facial image occlusion,
IVC(26), No. 7, 2 July 2008, pp. 1052-1067.
Elsevier DOI 0804
Facial expression recognition; Gabor filters; Discriminant Non-negative Matrix Factorization; Support Vector Machines; Partial occlusion BibRef

Buciu, I.[Ioan], Kotropoulos, C.[Constantine], Pitas, I.[Ioannis],
Comparison of ICA approaches for facial expression recognition,
SIViP(3), No. 4, December 2009, pp. xx-yy.
Springer DOI 0911
BibRef
Earlier:
ICA and gabor representation for facial expression recognition,
ICIP03(II: 855-858).
IEEE DOI 0312
BibRef
Earlier:
On the stability of support vector machines for face detection,
ICIP02(III: 121-124).
IEEE DOI 0210
BibRef
Earlier:
Combining Support Vector Machines for Accurate Face Detection,
ICIP01(I: 1054-1057).
IEEE DOI 0108
BibRef

Kotsia, I.[Irene], Patras, I.[Ioannis],
Support tucker machines,
CVPR11(633-640).
IEEE DOI 1106
BibRef
Earlier:
Multiplicative Update Rules for Multilinear Support Tensor Machines,
ICPR10(33-36).
IEEE DOI 1008

See also Relative Margin Support Tensor Machines for gait and action recognition. BibRef

Kotsia, I.[Irene], Zafeiriou, S.P.[Stefanos P.], Nikolaidis, N.[Nikolaos], Pitas, I.[Ioannis],
Texture and Shape Information Fusion for Facial Action Unit Recognition,
ACHI08(77-82).
IEEE DOI 0802
BibRef

Bishay, M., Patras, I.,
Fusing Multilabel Deep Networks for Facial Action Unit Detection,
FG17(681-688)
IEEE DOI 1707
Correlation, Databases, Face, Feature extraction, Gold, Neurons, Training BibRef

Zafeiriou, S.P.[Stefanos P.], Petrou, M.[Maria],
Sparse representations for facial expressions recognition via L1 optimization,
CVCGI10(32-39).
IEEE DOI 1006
BibRef

Yang, P.[Peng], Liu, Q.S.[Qing-Shan], Metaxas, D.N.[Dimitris N.],
Boosting encoded dynamic features for facial expression recognition,
PRL(30), No. 2, 15 January 2009, pp. 132-139.
Elsevier DOI 0804
BibRef
Earlier:
Similarity Features for Facial Event Analysis,
ECCV08(I: 685-696).
Springer DOI 0810
BibRef
Earlier:
Boosting Coded Dynamic Features for Facial Action Units and Facial Expression Recognition,
CVPR07(1-6).
IEEE DOI 0706
Pattern recognition; Facial expression; Video understanding; Boosting BibRef

Yang, P.[Peng], Liu, Q.S.[Qing-Shan], Metaxas, D.N.[Dimitris N.],
Dynamic soft encoded patterns for facial event analysis,
CVIU(115), No. 3, March 2011, pp. 456-465.
Elsevier DOI 1103
BibRef
Earlier:
RankBoost with L1 regularization for facial expression recognition and intensity estimation,
ICCV09(1018-1025).
IEEE DOI 0909
Facial expression; Dynamic feature; Time resolution BibRef

Yang, P.[Peng], Liu, Q.S.[Qing-Shan], Metaxas, D.N.[Dimitris N.],
Exploring facial expressions with compositional features,
CVPR10(2638-2644).
IEEE DOI 1006
BibRef

Yang, P.[Peng], Liu, Q.S.[Qing-Shan], Cui, X.Y.[Xin-Yi], Metaxas, D.N.[Dimitris N.],
Facial expression recognition using encoded dynamic features,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Koelstra, S.[Sander], Pantic, M.[Maja], Patras, I.[Ioannis],
A Dynamic Texture-Based Approach to Recognition of Facial Actions and Their Temporal Models,
PAMI(32), No. 11, November 2010, pp. 1940-1954.
IEEE DOI 1011
FAUs from texture. motion history images and nonrigid registration using free-form deformations. BibRef

Koelstra, S.[Sander], Pantic, M.[Maja],
Non-rigid registration using free-form deformations for recognition of facial actions and their temporal dynamics,
FG08(1-8).
IEEE DOI 0809
BibRef

Valstar, M.F.[Michel F.], Pantic, M.[Maja],
Fully Automatic Recognition of the Temporal Phases of Facial Actions,
SMC-B(42), No. 1, February 2012, pp. 28-43.
IEEE DOI 1201
BibRef
Earlier:
Combined Support Vector Machines and Hidden Markov Models for Modeling Facial Action Temporal Dynamics,
CVHCI07(118-127).
Springer DOI 0710
BibRef
Earlier:
Fully Automatic Facial Action Unit Detection and Temporal Analysis,
V4HCI06(149).
IEEE DOI 0609
BibRef

Sandbach, G.[Georgia], Zafeiriou, S.P.[Stefanos P.], Pantic, M.[Maja],
Local normal binary patterns for 3D facial action unit detection,
ICIP12(1813-1816).
IEEE DOI 1302
BibRef
Earlier:
Binary Pattern Analysis for 3D Facial Action Unit Detection,
BMVC12(119).
DOI Link 1301
BibRef

Martinez, B.[Brais], Valstar, M.F.[Michel F.], Binefa, X.[Xavier], Pantic, M.[Maja],
Local Evidence Aggregation for Regression-Based Facial Point Detection,
PAMI(35), No. 5, May 2013, pp. 1149-1163.
IEEE DOI 1304
BibRef
Earlier: A2, A1, A3, A4:
Facial point detection using boosted regression and graph models,
CVPR10(2729-2736).
IEEE DOI 1006
frontal and near-frontal. Use face shape to restrict feature point locations. BibRef

Martinez, B.[Brais], Valstar, M.F.[Michel F.],
L2,1-based regression and prediction accumulation across views for robust facial landmark detection,
IVC(47), No. 1, 2016, pp. 36-44.
Elsevier DOI 1605
Facial landmark detection BibRef

Sánchez-Lozano, E.[Enrique], Martinez, B.[Brais], Valstar, M.F.[Michel F.],
Cascaded regression with sparsified feature covariance matrix for facial landmark detection,
PRL(73), No. 1, 2016, pp. 19-25.
Elsevier DOI 1604
Supervised descent method BibRef

Sánchez-Lozano, E.[Enrique], Tzimiropoulos, G.[Georgios], Martinez, B., de la Torre, F.[Fernando], Valstar, M.F.[Michel F.],
A Functional Regression Approach to Facial Landmark Tracking,
PAMI(40), No. 9, September 2018, pp. 2037-2050.
IEEE DOI 1808
Face, Training, Shape, Real-time systems, Data models, Fourier series, Data analysis, Continuous regression, face tracking, functional data analysis BibRef

Sánchez-Lozano, E.[Enrique], Martinez, B.[Brais], Tzimiropoulos, G.[Georgios], Valstar, M.F.[Michel F.],
Cascaded Continuous Regression for Real-Time Incremental Face Tracking,
ECCV16(VIII: 645-661).
Springer DOI 1611
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Valstar, M.F., Patras, I.[Ioannis], Pantic, M.[Maja],
Facial Action Unit Detection using Probabilistic Actively Learned Support Vector Machines on Tracked Facial Point Data,
VHCI05(III: 76-76).
IEEE DOI 0507
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Savran, A.[Arman], Sankur, B.[Bülent], Bilge, M.T.[M. Taha],
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PR(45), No. 2, February 2012, pp. 767-782.
Elsevier DOI 1110
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Earlier:
Facial action unit detection: 3D versus 2D modality,
CVPR4HB10(71-78).
IEEE DOI 1006
3D expression recognition; 3D facial expression database; Action unit detection; Facial action coding system; Modality fusion; Gabor wavelets BibRef

Savran, A.[Arman], Sankur, B.[Bulent], Bilge, M.T.[M. Taha],
Regression-based intensity estimation of facial action units,
IVC(30), No. 10, October 2012, pp. 774-784.
Elsevier DOI 1210
Action unit intensity estimation; 3D facial expression recognition; Facial Action Coding System; Feature selection; AdaBoost.RT; SVM regression BibRef

Savran, A.[Arman], Sankur, B.[Bulent],
Automatic detection of facial actions from 3D data,
HCI09(1993-2000).
IEEE DOI 0910
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Savran, A.[Arman], Sankur, B.[Bülent],
Non-rigid registration based model-free 3D facial expression recognition,
CVIU(162), No. 1, 2017, pp. 146-165.
Elsevier DOI 1710
Facial expression recognition BibRef

Polikovsky, S.[Senya], Kameda, Y.[Yoshinari], Ohta, Y.[Yuichi],
Facial Micro-Expression Detection in Hi-Speed Video Based on Facial Action Coding System (FACS),
IEICE(E96-D), No. 1, January 2013, pp. 81-92.
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McDuff, D.J.[Daniel J.], el Kaliouby, R.[Rana], Picard, R.W.[Rosalind W.],
Crowdsourcing Facial Responses to Online Videos,
AffCom(3), No. 4 2012, pp. 456-468.
IEEE DOI 1302
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McDuff, D.J.[Daniel J.], el Kaliouby, R.[Rana], Kassam, K.[Karim], Picard, R.W.[Rosalind W.],
Affect valence inference from facial action unit spectrograms,
AMFG10(17-24).
IEEE DOI 1006
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Senechal, T., McDuff, D.J.[Daniel J.], el Kaliouby, R.[Rana],
Facial Action Unit Detection Using Active Learning and an Efficient Non-linear Kernel Approximation,
CV4AC15(10-18)
IEEE DOI 1602
Approximation methods BibRef

McDuff, D.J.[Daniel J.], el Kaliouby, R.[Rana],
Applications of Automated Facial Coding in Media Measurement,
AffCom(8), No. 2, April 2017, pp. 148-160.
IEEE DOI 1706
Encoding, Face, Media, Motion pictures, TV, Testing, Webcams, Facial expressions, advertising, crowdsourcing, emotion, facial coding, marketing, media, measurement BibRef

Aifanti, N.[Niki], Delopoulos, A.[Anastasios],
Linear subspaces for facial expression recognition,
SP:IC(29), No. 1, 2014, pp. 177-188.
Elsevier DOI 1402
Face analysis BibRef

Jiang, B.[Bihan], Valstar, M.F., Martinez, B., Pantic, M.,
A Dynamic Appearance Descriptor Approach to Facial Actions Temporal Modeling,
Cyber(44), No. 2, February 2014, pp. 161-174.
IEEE DOI 1403
Markov processes BibRef

Almaev, T.R., Martinez, B.[Brais], Valstar, M.F.[Michel F.],
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection,
ICCV15(3774-3782)
IEEE DOI 1602
Data models; Encoding; Face recognition; Facial muscles; Gold; Training BibRef

Mavadati, S.M.[S. Mohammad], Mahoor, M.H.[Mohammad H.],
Temporal Facial Expression Modeling for Automated Action Unit Intensity Measurement,
ICPR14(4648-4653)
IEEE DOI 1412
Accuracy BibRef

Mahoor, M.H.[Mohammad H.], Cadavid, S.[Steven], Messinger, D.S.[Daniel S.], Cohn, J.F.[Jeffrey F.],
A framework for automated measurement of the intensity of non-posed Facial Action Units,
CVPR4HB09(74-80).
IEEE DOI 0906
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Wu, B.Y.[Bao-Yuan], Lyu, S.W.[Si-Wei], Hu, B.G.[Bao-Gang], Ji, Q.A.[Qi-Ang],
Multi-label learning with missing labels for image annotation and facial action unit recognition,
PR(48), No. 7, 2015, pp. 2279-2289.
Elsevier DOI 1504
Multi-label learning BibRef

Li, Y.Q.[Yong-Qiang], Wu, B.Y.[Bao-Yuan], Ghanem, B.[Bernard], Zhao, Y.P.[Yong-Ping], Yao, H.X.[Hong-Xun], Ji, Q.A.[Qi-Ang],
Facial action unit recognition under incomplete data based on multi-label learning with missing labels,
PR(60), No. 1, 2016, pp. 890-900.
Elsevier DOI 1609
Face action unit recognition BibRef

Wu, B.Y.[Bao-Yuan], Jia, F.[Fan], Liu, W.[Wei], Ghanem, B.[Bernard], Lyu, S.W.[Si-Wei],
Multi-label Learning with Missing Labels Using Mixed Dependency Graphs,
IJCV(126), No. 8, August 2018, pp. 875-896.
Springer DOI 1807
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Wu, B.Y.[Bao-Yuan], Liu, Z.L.[Zhi-Lei], Wang, S.F.[Shang-Fei], Hu, B.G.[Bao-Gang], Ji, Q.A.[Qi-Ang],
Multi-label Learning with Missing Labels,
ICPR14(1964-1968)
IEEE DOI 1412
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Wu, B.Y.[Bao-Yuan], Lyu, S.W.[Si-Wei], Ghanem, B.[Bernard],
ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph,
ICCV15(4157-4165)
IEEE DOI 1602
multi-label learning with missing labels. Games; Horses; Image edge detection; Semantics; Training BibRef

Zhong, L.[Lin], Liu, Q.S.[Qing-Shan], Yang, P.[Peng], Huang, J.Z.[Jun-Zhou], Metaxas, D.N.[Dimitris N.],
Learning Multiscale Active Facial Patches for Expression Analysis,
Cyber(45), No. 8, August 2015, pp. 1499-1510.
IEEE DOI 1506
Face recognition BibRef

Zhong, L.[Lin], Liu, Q.S.[Qing-Shan], Yang, P.[Peng], Liu, B.[Bo], Huang, J.Z.[Jun-Zhou], Metaxas, D.N.[Dimitris N.],
Learning active facial patches for expression analysis,
CVPR12(2562-2569).
IEEE DOI 1208
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Zhao, K.[Kaili], Chu, W.S.[Wen-Sheng], de la Torre, F.[Fernando], Cohn, J.F.[Jeffrey F.], Zhang, H.G.[Hong-Gang],
Joint Patch and Multi-label Learning for Facial Action Unit and Holistic Expression Recognition,
IP(25), No. 8, August 2016, pp. 3931-3946.
IEEE DOI 1608
BibRef
Earlier:
Joint patch and multi-label learning for facial action unit detection,
CVPR15(2207-2216)
IEEE DOI 1510
face recognition BibRef

Chu, W.S.[Wen-Sheng], de la Torre, F.[Fernando], Cohn, J.F.[Jeffrey F.],
Learning facial action units with spatiotemporal cues and multi-label sampling,
IVC(81), 2019, pp. 1-14.
Elsevier DOI 1902
BibRef
Earlier:
Learning Spatial and Temporal Cues for Multi-Label Facial Action Unit Detection,
FG17(25-32)
IEEE DOI 1707
Multi-label learning, Deep learning, Spatio-temporal learning, Multi-label sampling, Facial action unit detection, Video analysis. Context, Correlation, Face, Feature extraction, Image color analysis. BibRef

Zhao, K.[Kaili], Chu, W.S.[Wen-Sheng], Zhang, H.G.[Hong-Gang],
Deep Region and Multi-label Learning for Facial Action Unit Detection,
CVPR16(3391-3399)
IEEE DOI 1612
BibRef

Girard, J.M., Cohn, J.F., Jeni, L.A.[László A.], Lucey, S., de la Torre, F.,
How much training data for facial action unit detection?,
FG15(1-8)
IEEE DOI 1508
face recognition BibRef

Nicolle, J.[Jérémie], Bailly, K.[Kévin], Chetouani, M.[Mohamed],
Real-time facial action unit intensity prediction with regularized metric learning,
IVC(52), No. 1, 2016, pp. 1-14.
Elsevier DOI 1609
Facial expression BibRef

Nicolle, J.[Jérémie], Bailly, K.[Kévin], Rapp, V.[Vincent], Chetouani, M.[Mohamed],
Locating facial landmarks with binary map cross-correlations,
ICIP13(2978-2982)
IEEE DOI 1402
LBP; LPQ; binary maps; facial landmarks; shape model BibRef

Zeng, J., Chu, W.S.[Wen-Sheng], de la Torre, F.[Fernando], Cohn, J.F.[Jeffery F.], Xiong, Z.,
Confidence Preserving Machine for Facial Action Unit Detection,
IP(25), No. 10, October 2016, pp. 4753-4767.
IEEE DOI 1610
BibRef
Earlier: ICCV15(3622-3630)
IEEE DOI 1602
face recognition. Boosting BibRef

Chu, W.S.[Wen-Sheng], de la Torre, F.[Fernando], Cohn, J.F.[Jeffery F.],
Selective Transfer Machine for Personalized Facial Expression Analysis,
PAMI(39), No. 3, March 2017, pp. 529-545.
IEEE DOI 1702
BibRef
Earlier:
Selective Transfer Machine for Personalized Facial Action Unit Detection,
CVPR13(3515-3522)
IEEE DOI 1309
Facial action unit detection; SVM; domain adaptation BibRef

Rahman, A.K.M.M.[A.K.M. Mahbubur], Anam, A.I.[Asm Iftekhar], Yeasin, M.[Mohammed],
A Unified Framework for Dividing and Predicting a Large Set of Action Units,
AffCom(7), No. 4, October 2016, pp. 311-325.
IEEE DOI 1612
Computational modeling BibRef

Rudovic, O.[Ognjen], Pantic, M.[Maja], Patras, I.Y.[Ioannis Yiannis],
Coupled Gaussian Processes for Pose-Invariant Facial Expression Recognition,
PAMI(35), No. 6, June 2013, pp. 1357-1369.
IEEE DOI 1305
BibRef
Earlier: A1, A3, A2:
Coupled Gaussian Process Regression for Pose-Invariant Facial Expression Recognition,
ECCV10(II: 350-363).
Springer DOI 1009
BibRef
Earlier: A1, A3, A2:
Regression-Based Multi-view Facial Expression Recognition,
ICPR10(4121-4124).
IEEE DOI 1008
BibRef

Eleftheriadis, S.[Stefanos], Rudovic, O.[Ognjen], Pantic, M.[Maja],
Discriminative Shared Gaussian Processes for Multiview and View-Invariant Facial Expression Recognition,
IP(24), No. 1, January 2015, pp. 189-204.
IEEE DOI 1502
BibRef
Earlier:
View-Constrained Latent Variable Model for Multi-view Facial Expression Classification,
ISVC14(II: 292-303).
Springer DOI 1501
BibRef
Earlier:
Shared Gaussian Process Latent Variable Model for Multi-view Facial Expression Recognition,
ISVC13(I:527-538).
Springer DOI 1310
Gaussian processes BibRef

Eleftheriadis, S.[Stefanos], Rudovic, O.[Ognjen], Pantic, M.[Maja],
Joint Facial Action Unit Detection and Feature Fusion: A Multi-Conditional Learning Approach,
IP(25), No. 12, December 2016, pp. 5727-5742.
IEEE DOI 1612
BibRef
Earlier:
Multi-conditional Latent Variable Model for Joint Facial Action Unit Detection,
ICCV15(3792-3800)
IEEE DOI 1602
Bayes methods BibRef

Eleftheriadis, S.[Stefanos], Rudovic, O.[Ognjen], Deisenroth, M.P.[Marc Peter], Pantic, M.[Maja],
Gaussian Process Domain Experts for Modeling of Facial Affect,
IP(26), No. 10, October 2017, pp. 4697-4711.
IEEE DOI 1708
BibRef
Earlier:
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units,
ACCV16(II: 154-170).
Springer DOI 1704
BibRef
Earlier:
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis,
Affect16(1469-1477)
IEEE DOI 1612
Gaussian processes, emotion recognition, face recognition, Gaussian process domain, facial behavior analysis, facial expression analysis, probabilistic framework, supervised domain adaptation, Adaptation models, Analytical models, Computational modeling, Context, Data models, Gold, Training, Domain adaptation, Gaussian processes, multi-view facial expression recognition, multiple AU detection BibRef

Yang, S.[Shuang], Rudovic, O.[Ognjen], Pavlovic, V.[Vladimir], Pantic, M.[Maja],
Personalized Modeling of Facial Action Unit Intensity,
ISVC14(II: 269-281).
Springer DOI 1501
BibRef

Rudovic, O.[Ognjen], Pavlovic, V.[Vladimir], Pantic, M.[Maja],
Context-Sensitive Dynamic Ordinal Regression for Intensity Estimation of Facial Action Units,
PAMI(37), No. 5, May 2015, pp. 944-958.
IEEE DOI 1504
BibRef
Earlier:
Context-Sensitive Conditional Ordinal Random Fields for Facial Action Intensity Estimation,
HACI13(492-499)
IEEE DOI 1403
BibRef
And:
Automatic Pain Intensity Estimation with Heteroscedastic Conditional Ordinal Random Fields,
ISVC13(II:234-243).
Springer DOI 1311
BibRef
Earlier:
Kernel Conditional Ordinal Random Fields for Temporal Segmentation of Facial Action Units,
Face12(II: 260-269).
Springer DOI 1210
BibRef
Earlier:
Multi-Output Laplacian Dynamic Ordinal Regression for Facial Expression Recognition and Intensity Estimation,
CVPR12(2634-2641).
IEEE DOI 1208
Context. emotion recognition
See also Structured Output Ordinal Regression for Dynamic Facial Emotion Intensity Prediction. BibRef

Walecki, R.[Robert], Rudovic, O.[Ognjen], Pavlovic, V.[Vladimir], Pantic, M.[Maja],
Copula Ordinal Regression Framework for Joint Estimation of Facial Action Unit Intensity,
AffCom(10), No. 3, July 2019, pp. 297-312.
IEEE DOI 1909
Gold, Computational modeling, Estimation, Data models, Noise measurement, Face, Neural networks, conditional random fields BibRef

Walecki, R.[Robert], Rudovic, O.[Ognjen], Pavlovic, V.[Vladimir], Schuller, B., Pantic, M.[Maja],
Deep Structured Learning for Facial Action Unit Intensity Estimation,
CVPR17(5709-5718)
IEEE DOI 1711
Data models, Estimation, Face, Feature extraction, Gold, Machine, learning BibRef

Walecki, R.[Robert], Rudovic, O.[Ognjen], Pantic, M.[Maja], Pavlovic, V.[Vladimir], Cohn, J.F.,
A Framework for Joint Estimation and Guided Annotation of Facial Action Unit Intensity,
Affect16(1460-1468)
IEEE DOI 1612
BibRef

Walecki, R.[Robert], Rudovic, O.[Ognjen], Pavlovic, V.[Vladimir], Pantic, M.[Maja],
Variable-state Latent Conditional Random Field models for facial expression analysis,
IVC(58), No. 1, 2017, pp. 25-37.
Elsevier DOI 1703
BibRef
And:
Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity,
CVPR16(4902-4910)
IEEE DOI 1612
Facial expression BibRef

Sandbach, G., Zafeiriou, S.P., Pantic, M.,
Markov Random Field Structures for Facial Action Unit Intensity Estimation,
SocialInter13(738-745)
IEEE DOI 1403
Markov processes BibRef

Walecki, R., Rudovic, O., Pavlovic, V., Pantic, M.,
Variable-state latent conditional random fields for facial expression recognition and action unit detection,
FG15(1-8)
IEEE DOI 1508
emotion recognition BibRef

Kaltwang, S.[Sebastian], Todorovic, S.[Sinisa], Pantic, M.[Maja],
Latent trees for estimating intensity of Facial Action Units,
CVPR15(296-304)
IEEE DOI 1510
BibRef

Lekdioui, K.[Khadija], Messoussi, R.[Rochdi], Ruichek, Y.[Yassine], Chaabi, Y.[Youness], Touahni, R.[Raja],
Facial decomposition for expression recognition using texture/shape descriptors and SVM classifier,
SP:IC(58), No. 1, 2017, pp. 300-312.
Elsevier DOI 1710
Facial components BibRef

Slimani, K., Bourekkadi, S., Messoussi, R., Ruichek, Y., Touahni, R.,
Sharing Emotions in the Distance Education Experience: Attitudes and Motivation of University Students,
ISCV20(1-10)
IEEE DOI 2011
cognition, computer aided instruction, decision making, distance learning, educational institutions, groupware, university. BibRef

Mohammadi, M.R.[Mohammad Reza], Fatemizadeh, E.[Emad], Mahoor, M.H.[Mohammad H.],
PCA-based dictionary building for accurate facial expression recognition via sparse representation,
JVCIR(25), No. 5, 2014, pp. 1082-1092.
Elsevier DOI 1406
BibRef
And:
Simultaneous recognition of facial expression and identity via sparse representation,
WACV14(1066-1073)
IEEE DOI 1406
Facial expression recognition. Dictionaries BibRef

Mohammadi, M.R.[Mohammad Reza], Fatemizadeh, E.[Emad], Mahoor, M.H.[Mohammad H.],
A joint dictionary learning and regression model for intensity estimation of facial AUs,
JVCIR(47), No. 1, 2017, pp. 1-9.
Elsevier DOI 1706
Facial action units. BibRef

Gupta, R.[Rahul], Audhkhasi, K.[Kartik], Jacokes, Z.[Zach], Rozga, A.[Agata], Narayanan, S.[Shrikanth],
Modeling Multiple Time Series Annotations as Noisy Distortions of the Ground Truth: An Expectation-Maximization Approach,
AffCom(9), No. 1, January 2018, pp. 76-89.
IEEE DOI 1804
behavioural sciences computing, expectation-maximisation algorithm, feature extraction, multiple annotators BibRef

Kim, S.T.[Seong Tae], Ro, Y.M.[Yong Man],
Facial Dynamics Interpreter Network: What Are the Important Relations Between Local Dynamics for Facial Trait Estimation?,
ECCV18(XII: 475-491).
Springer DOI 1810
BibRef

Li, W.[Wei], Abtahi, F.[Farnaz], Zhu, Z.G.[Zhi-Gang], Yin, L.J.[Li-Jun],
EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection,
PAMI(40), No. 11, November 2018, pp. 2583-2596.
IEEE DOI 1810
BibRef
Earlier:
EAC-Net: A Region-Based Deep Enhancing and Cropping Approach for Facial Action Unit Detection,
FG17(103-110)
IEEE DOI 1707
Lips, Face, Feature extraction, Convolutional codes, Encoding, Robustness, Convolutional neural network, facial analysis, head poses. Face, Robustness, Training BibRef

Li, W.[Wei], Abtahi, F.[Farnaz], Zhu, Z.G.[Zhi-Gang],
Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing,
CVPR17(6766-6775)
IEEE DOI 1711
Face, Feature extraction, Gold, Machine learning, Muscles, Neural networks, Training BibRef

Wang, S.F.[Shang-Fei], Peng, G.Z.[Guo-Zhu], Chen, S.Y.[Shi-Yu], Ji, Q.[Qiang],
Weakly Supervised Facial Action Unit Recognition With Domain Knowledge,
Cyber(48), No. 11, November 2018, pp. 3265-3276.
IEEE DOI 1810
Gold, Training, Databases, Face recognition, Face, Supervised learning, Cybernetics, Domain knowledge, weakly supervised learning BibRef

Wang, J.[Jiahe], Ding, H.[Heyan], Wang, S.F.[Shang-Fei],
Occluded Facial Expression Recognition Using Self-supervised Learning,
ACCV22(IV:121-136).
Springer DOI 2307
BibRef

Chang, Y.[Yanan], Wang, S.F.[Shang-Fei],
Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition,
CVPR22(20385-20394)
IEEE DOI 2210
Representation learning, Gold, Correlation, Databases, Annotations, Face recognition, Supervised learning, Face and gestures, Self- semi- meta- unsupervised learning BibRef

Wang, S.F.[Shang-Fei], Peng, G.Z.[Guo-Zhu],
Weakly Supervised Dual Learning for Facial Action Unit Recognition,
MultMed(21), No. 12, December 2019, pp. 3218-3230.
IEEE DOI 1912
BibRef
Earlier: A2, A1:
Weakly Supervised Facial Action Unit Recognition Through Adversarial Training,
CVPR18(2188-2196)
IEEE DOI 1812
Task analysis, Face, Face recognition, Learning systems, Image recognition, Generators, Action unit recognition, dual learning. Training, Image recognition, Pain, Supervised learning, Muscles BibRef

Wang, S.F.[Shang-Fei], Gan, Q.[Quan], Ji, Q.A.[Qi-Ang],
Expression-assisted facial action unit recognition under incomplete AU annotation,
PR(61), No. 1, 2017, pp. 78-91.
Elsevier DOI 1609
AU recognition BibRef

Wang, S.F.[Shang-Fei], Wu, Y.[Yi], Chang, Y.[Yanan], Li, G.M.[Guo-Ming], Mao, M.[Meng],
Pose-Aware Facial Expression Recognition Assisted by Expression Descriptions,
AffCom(15), No. 1, January 2024, pp. 241-253.
IEEE DOI 2403
Feature extraction, Face recognition, Lips, Faces, Adversarial machine learning, Semantics, Multitasking, Pose-aware, cross-modality attention BibRef

Wang, S.F.[Shang-Fei], Ding, H.[Heyan], Peng, G.Z.[Guo-Zhu],
Dual Learning for Facial Action Unit Detection Under Nonfull Annotation,
Cyber(52), No. 4, April 2022, pp. 2225-2237.
IEEE DOI 2204
Gold, Task analysis, Face, Annotations, Face recognition, Image reconstruction, Probabilistic logic, Adversarial learning, weakly supervised BibRef

Wang, S.F.[Shang-Fei], Chang, Y.[Yanan], Wang, C.[Can],
Dual Learning for Joint Facial Landmark Detection and Action Unit Recognition,
AffCom(14), No. 2, April 2023, pp. 1404-1416.
IEEE DOI 2306
Gold, Task analysis, Face recognition, Feature extraction, Correlation, Learning systems, Image recognition, dual learning BibRef

Wang, S.F.[Shang-Fei], Wu, S.[Shan], Peng, G.Z.[Guo-Zhu], Ji, Q.A.[Qi-Ang],
Capturing Feature and Label Relations Simultaneously for Multiple Facial Action Unit Recognition,
AffCom(10), No. 3, July 2019, pp. 348-359.
IEEE DOI 1909
BibRef
Earlier: A2, A1, A4, Only:
Multiple Facial Action Unit recognition by learning joint features and label relations,
ICPR16(2246-2251)
IEEE DOI 1705
Databases, Gold, Face recognition, Affective computing, Auditory system, AU recognition, RBM. Bayes methods, Face recognition, Facial features, Gold, Inference algorithms, Kernel, Training BibRef

Li, Y.Q.[Yong-Qiang], Wang, S.F.[Shang-Fei], Zhao, Y.P.[Yong-Ping], Ji, Q.A.[Qi-Ang],
Simultaneous Facial Feature Tracking and Facial Expression Recognition,
IP(22), No. 7, 2013, pp. 2559-2573.
IEEE DOI 1307
BibRef
Earlier: A1, A3, A4, Only:
Simultaneous facial activity tracking and recognition,
ICPR12(1017-1020).
WWW Link. 1302
shape recognition; eyebrow; facial action units BibRef

He, S.[Shan], Wang, S.F.[Shang-Fei], Lv, Y.P.[Yan-Peng],
Spontaneous Facial Expression Recognition Based on Feature Point Tracking,
ICIG11(760-765).
IEEE DOI 1109
BibRef

Yang, J.J.[Jia-Jia], Wu, S.[Shan], Wang, S.F.[Shang-Fei], Ji, Q.[Qiang],
Multiple facial action unit recognition enhanced by facial expressions,
ICPR16(4089-4094)
IEEE DOI 1705
Databases, Face recognition, Gold, Mathematical model, Probabilistic logic, Semantics, Training BibRef

Wang, Z.H.[Zi-Heng], Li, Y.Q.[Yong-Qiang], Wang, S.F.[Shang-Fei], Ji, Q.A.[Qi-Ang],
Capturing Global Semantic Relationships for Facial Action Unit Recognition,
ICCV13(3304-3311)
IEEE DOI 1403
AU recognition; RBM; Spatiotemporal relationship BibRef

Zhang, Y.[Yong], Dong, W.M.[Wei-Ming], Hu, B.G.[Bao-Gang], Ji, Q.[Qiang],
Classifier Learning with Prior Probabilities for Facial Action Unit Recognition,
CVPR18(5108-5116)
IEEE DOI 1812
Gold, Training, Correlation, Muscles, Face recognition, Linear programming BibRef

Wang, S.F.[Shang-Fei], Hao, L.F.[Long-Fei], Ji, Q.[Qiang],
Facial Action Unit Recognition and Intensity Estimation Enhanced Through Label Dependencies,
IP(28), No. 3, March 2019, pp. 1428-1442.
IEEE DOI 1812
Bayes methods, belief networks, emotion recognition, face recognition, learning (artificial intelligence), label dependencies BibRef

Wang, S.F.[Shang-Fei], Pan, B.[Bowen], Wu, S.[Shan], Ji, Q.[Qiang],
Deep Facial Action Unit Recognition and Intensity Estimation from Partially Labelled Data,
AffCom(12), No. 4, October 2021, pp. 1018-1030.
IEEE DOI 2112
Estimation, Face recognition, Image recognition, Support vector machines, Training data, Databases, AU recognition, partially labelled data BibRef

Zhang, Y.[Yong], Jiang, H.Y.[Hai-Yong], Wu, B.Y.[Bao-Yuan], Fan, Y.B.[Yan-Bo], Ji, Q.[Qiang],
Context-Aware Feature and Label Fusion for Facial Action Unit Intensity Estimation With Partially Labeled Data,
ICCV19(733-742)
IEEE DOI 2004
face recognition, feature extraction, image fusion, image representation, image segmentation, Face BibRef

Hao, L., Wang, S., Peng, G., Ji, Q.,
Facial Action Unit Recognition Augmented by Their Dependencies,
FG18(187-194)
IEEE DOI 1806
Bayes methods, Databases, Face recognition, Gold, Muscles, Probabilistic logic, Training, Facial Action Unit Recognition, Latent Regression Bayesian Networks BibRef

Wang, S.F.[Shang-Fei], Yang, J.J.[Jia-Jia], Gao, Z.[Zhen], Ji, Q.A.[Qi-Ang],
Feature and label relation modeling for multiple-facial action unit classification and intensity estimation,
PR(65), No. 1, 2017, pp. 71-81.
Elsevier DOI 1702
AU recognition BibRef

Zhu, Y.C.[Ya-Chen], Wang, S.F.[Shang-Fei], Yue, L.H.[Li-Hua], Ji, Q.A.[Qi-Ang],
Multiple-Facial Action Unit Recognition by Shared Feature Learning and Semantic Relation Modeling,
ICPR14(1663-1668)
IEEE DOI 1412
Accuracy BibRef

Tong, Y.[Yan], Liao, W.H.[Wen-Hui], Ji, Q.A.[Qi-Ang],
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships,
PAMI(29), No. 10, October 2007, pp. 1683-1699.
IEEE DOI 0710
Integrate relations, dynamics, and appearance.
See also Robust facial feature tracking under varying face pose and facial expression. BibRef

Li, Y.Q.[Yong-Qiang], Chen, J.X.[Ji-Xu], Zhao, Y.P.[Yong-Ping], Ji, Q.A.[Qi-Ang],
Data-Free Prior Model for Facial Action Unit Recognition,
AffCom(4), No. 2, 2013, pp. 127-141.
IEEE DOI 1307
Computational modeling BibRef

Wu, S.[Shan], Wang, S.F.[Shang-Fei], Pan, B., Ji, Q.A.[Qi-Ang],
Deep Facial Action Unit Recognition from Partially Labeled Data,
ICCV17(3971-3979)
IEEE DOI 1802
Boltzmann machines, face recognition, learning (artificial intelligence), minimisation, Training data BibRef

Xiang, X., Tran, T.D.,
Linear Disentangled Representation Learning for Facial Actions,
CirSysVideo(28), No. 12, December 2018, pp. 3539-3544.
IEEE DOI 1812
Face, Videos, Computational modeling, Training, Dictionaries, Face recognition, Training data, Face video, facial expression, low-rank BibRef

Wang, F.Y.[Feng-Yuan], Lv, J.H.[Jian-Hua], Ying, G.[Guode], Chen, S.H.[Sheng-Hui], Zhang, C.[Chi],
Facial expression recognition from image based on hybrid features understanding,
JVCIR(59), 2019, pp. 84-88.
Elsevier DOI 1903
Facial expression recognition, Convolutional neural networks, Scale-invariant feature transform, Deep-learning feature, Support vector machines BibRef

Lifkooee, M.Z.[Masoud Z.], Soysal, Ö.M.[Ömer M.], Sekeroglu, K.[Kazim],
Video mining for facial action unit classification using statistical spatial-temporal feature image and LoG deep convolutional neural network,
MVA(30), No. 1, February 2019, pp. 41-57.
WWW Link. 1904
BibRef

Meng, Z., Han, S., Liu, P., Tong, Y.,
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion,
Cyber(49), No. 9, Sep. 2019, pp. 3293-3306.
IEEE DOI 1906
Visualization, Face recognition, Speech recognition, Feature extraction, Physiology, Semantics, speech related facial action unit (AU) recognition BibRef

Martinez, B., Valstar, M.F., Jiang, B., Pantic, M.,
Automatic Analysis of Facial Actions: A Survey,
AffCom(10), No. 3, July 2019, pp. 325-347.
IEEE DOI 1909
Encoding, Psychology, Face, Face recognition, Computer science, Databases, Feature extraction, Action Unit analysis, survey BibRef

Benitez-Quiroz, C.F.[C. Fabian], Srinivasan, R.[Ramprakash], Martinez, A.M.[Aleix M.],
Discriminant Functional Learning of Color Features for the Recognition of Facial Action Units and Their Intensities,
PAMI(41), No. 12, December 2019, pp. 2835-2845.
IEEE DOI 1911
Image color analysis, Face recognition, Video sequences, Videos, Transforms, Gabor transforms, Facial expressions of emotion, recognition in still images BibRef

Zhao, K., Chu, W., Martinez, A.M.,
Learning Facial Action Units from Web Images with Scalable Weakly Supervised Clustering,
CVPR18(2090-2099)
IEEE DOI 1812
Gold, Detectors, Training, Semantics, Hidden Markov models, Noise measurement, Support vector machines BibRef

Benitez-Quiroz, C.F.[C. Fabian], Wang, Y., Martinez, A.M.[Aleix M.],
Recognition of Action Units in the Wild with Deep Nets and a New Global-Local Loss,
ICCV17(3990-3999)
IEEE DOI 1802
convergence, face recognition, feature extraction, image denoising, learning (artificial intelligence), neural nets, BibRef

Yang, Y.[Yu], Gao, X.G.[Xiao-Guang], Guo, Z.G.[Zhi-Gao], Chen, D.Q.[Da-Qing],
Learning Bayesian networks using the constrained maximum a posteriori probability method,
PR(91), 2019, pp. 123-134.
Elsevier DOI 1904
Bayesian network, Parameter learning, Expert judgment, Facial action unit BibRef

Sankaran, N.[Nishant], Mohan, D.D.[Deen Dayal], Lakshminarayana, N.N.[Nagashri N.], Setlur, S.[Srirangaraj], Govindaraju, V.[Venu],
Domain adaptive representation learning for facial action unit recognition,
PR(102), 2020, pp. 107127.
Elsevier DOI 2003
Feature fusion, Feature fine-tuning, Facial action unit recognition, Deep fusion, Multi-Modal representation learning BibRef

Lakshminarayana, N.N.[Nagashri N.], Setlur, S.[Srirangaraj], Govindaraju, V.[Venu],
Learning Guided Attention Masks for Facial Action Unit Recognition,
FG20(465-472)
IEEE DOI 2102
Gold, Face recognition, Task analysis, Lips, Eyebrows, Feature extraction, Training, Facial action unit recognition, spatial attention BibRef

Zhi, R.C.[Rui-Cong], Liu, M.Y.[Meng-Yi], Zhang, D.Z.[De-Zheng],
A comprehensive survey on automatic facial action unit analysis,
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Springer DOI 2005
Survey, Facial Action Unit. BibRef

Talele, K.[Kiran], Tuckley, K.[Kushal],
Facial expression recognition using digital signature feature descriptor,
SIViP(14), No. 4, June 2020, pp. 701-709.
WWW Link. 2005
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Ben Tanfous, A.[Amor], Drira, H.[Hassen], Ben Amor, B.[Boulbaba],
Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition,
PAMI(42), No. 10, October 2020, pp. 2594-2607.
IEEE DOI 2009
Manifolds, Shape, Face recognition, Trajectory, Encoding, facial expression recognition BibRef

Taha, B., Hayat, M., Berretti, S., Hatzinakos, D., Werghi, N.,
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection,
CirSysVideo(30), No. 9, September 2020, pp. 2900-2916.
IEEE DOI 2009
Shape, Face, Task analysis, Computational modeling, Solid modeling, transfer learning BibRef

Cao, S.[Shan], Yao, Y.Q.[Yu-Qian], An, G.Y.[Gao-Yun],
E2-capsule neural networks for facial expression recognition using AU-aware attention,
IET-IPR(14), No. 11, September 2020, pp. 2417-2424.
DOI Link 2009
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Wang, S., Peng, G., Ji, Q.,
Exploring Domain Knowledge for Facial Expression-Assisted Action Unit Activation Recognition,
AffCom(11), No. 4, October 2020, pp. 640-652.
IEEE DOI 2011
Gold, Face recognition, Image recognition, Training, Bayes methods, Probabilistic logic, Databases, AU activation recognition, RBM BibRef

Shao, Z.W.[Zhi-Wen], Liu, Z.L.[Zhi-Lei], Cai, J.F.[Jian-Fei], Ma, L.Z.[Li-Zhuang],
JÂA-Net: Joint Facial Action Unit Detection and Face Alignment Via Adaptive Attention,
IJCV(129), No. 2, February 2021, pp. 321-340. 2102
BibRef
Earlier:
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment,
ECCV18(XIII: 725-740).
Springer DOI 1810
BibRef

Shao, Z.W.[Zhi-Wen], Liu, Z.L.[Zhi-Lei], Cai, J.F.[Jian-Fei], Wu, Y.S.[Yun-Sheng], Ma, L.Z.[Li-Zhuang],
Facial Action Unit Detection Using Attention and Relation Learning,
AffCom(13), No. 3, July 2022, pp. 1274-1289.
IEEE DOI 2209
Gold, Feature extraction, Estimation, Face, Deep learning, Computer science, Learning systems, facial AU detection BibRef

Shao, Z.W.[Zhi-Wen], Zhou, Y.[Yong], Cai, J.F.[Jian-Fei], Zhu, H.C.[Han-Cheng], Yao, R.[Rui],
Facial Action Unit Detection via Adaptive Attention and Relation,
IP(32), 2023, pp. 3354-3366.
IEEE DOI 2307
Gold, Feature extraction, Adaptive systems, Correlation, Convolutional neural networks, Cognition, Location awareness, adaptive spatio-temporal graph convolutional network BibRef

Shao, Z.W.[Zhi-Wen], Cai, J.F.[Jian-Fei], Cham, T.J.[Tat-Jen], Lu, X.Q.[Xue-Quan], Ma, L.Z.[Li-Zhuang],
Unconstrained Facial Action Unit Detection via Latent Feature Domain,
AffCom(13), No. 2, April 2022, pp. 1111-1126.
IEEE DOI 2206
Gold, Feature extraction, Annotations, Training, Games, Face recognition, Correlation, Unconstrained facial AU detection, feature disentanglement BibRef

Tang, J.S.[Jun-Shu], Shao, Z.W.[Zhi-Wen], Ma, L.Z.[Li-Zhuang],
EGGAN: Learning Latent Space for Fine-Grained Expression Manipulation,
MultMedMag(28), No. 3, July 2021, pp. 42-51.
IEEE DOI 2109
Learning systems, Generative adversarial networks, Training data, Facial recognition, Semantics, Facial features, Generative models, structured latent space BibRef

Chen, Y.D.[Yue-Dong], Song, G.X.[Guo-Xian], Shao, Z.W.[Zhi-Wen], Cai, J.F.[Jian-Fei], Cham, T.J.[Tat-Jen], Zheng, J.M.[Jian-Min],
GeoConv: Geodesic guided convolution for facial action unit recognition,
PR(122), 2022, pp. 108355.
Elsevier DOI 2112
Geodesic guided convolution, 3D morphable face model, Facial action unit recognition, Emotion recognition BibRef

Li, Y.[Yong], Zeng, J.[Jiabei], Shan, S.G.[Shi-Guang],
Learning Representations for Facial Actions From Unlabeled Videos,
PAMI(44), No. 1, January 2022, pp. 302-317.
IEEE DOI 2112
Gold, Face, Feature extraction, Videos, Magnetic heads, Task analysis, Facial action unit detection, self-supervised learning, encoder-decoder structure BibRef

Li, Y.[Yong], Shan, S.G.[Shi-Guang],
Contrastive Learning of Person-Independent Representations for Facial Action Unit Detection,
IP(32), 2023, pp. 3212-3225.
IEEE DOI 2306
Gold, Videos, Training, Image reconstruction, Feature extraction, Faces, Task analysis, Facial action unit detection, person-independent action unit detection BibRef

Li, Y.[Yong], Shan, S.G.[Shi-Guang],
Meta Auxiliary Learning for Facial Action Unit Detection,
AffCom(14), No. 3, July 2023, pp. 2526-2538.
IEEE DOI 2310
BibRef

Sun, X.[Xuran], Zeng, J.[Jiabei], Shan, S.G.[Shi-Guang],
Emotion-aware Contrastive Learning for Facial Action Unit Detection,
FG21(01-08)
IEEE DOI 2303
Training, Protocols, Face recognition, Diversity reception, Gesture recognition BibRef

Li, Y.[Yong], Zeng, J.[Jiabei], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Self-Supervised Representation Learning From Videos for Facial Action Unit Detection,
CVPR19(10916-10925).
IEEE DOI 2002
BibRef

Jia, X.B.[Xi-Bin], Zhou, Y.H.[Yu-Han], Li, W.T.[Wei-Ting], Li, J.H.[Jing-Hua], Yin, B.C.[Bao-Cai],
Data-aware relation learning-based graph convolution neural network for facial action unit recognition,
PRL(155), 2022, pp. 100-106.
Elsevier DOI 2203
AU recognition, FACS, GCN, Relation representation, Metric learning BibRef

Jia, X.B.[Xi-Bin], Xu, S.W.[Shao-Wu], Zhou, Y.H.[Yu-Han], Wang, L.[Luo], Li, W.T.[Wei-Ting],
A novel dual-channel graph convolutional neural network for facial action unit recognition,
PRL(166), 2023, pp. 61-68.
Elsevier DOI 2302
FACS, GCN, Metric learning, AU relation, Dual-channel graph convolutional neural network BibRef

Romero, A.[Andrés], León, J.[Juán], Arbeláez, P.[Pablo],
Multi-view dynamic facial action unit detection,
IVC(122), 2022, pp. 103723.
Elsevier DOI 2205
BibRef

Song, W.Y.[Wen-Yu], Shi, S.[Shuze], Wu, Y.X.[Yu-Xuan], An, G.Y.[Gao-Yun],
Dual-attention guided network for facial action unit detection,
IET-IPR(16), No. 8, 2022, pp. 2157-2170.
DOI Link 2205
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Hu, Q.P.[Qiao-Ping], Mei, C.N.[Chuan-Neng], Jiang, F.[Fei], Shen, R.M.[Rui-Min], Zhang, Y.T.[Yi-Tian], Wang, C.[Ce], Zhang, J.P.[Jun-Peng],
RFAU: A Database for Facial Action Unit Analysis in Real Classrooms,
AffCom(13), No. 3, July 2022, pp. 1452-1465.
IEEE DOI 2209
Databases, Lighting, Head, Gold, Estimation, Annotations, Education, Facial action unit, database, classroom, juvenile BibRef

Gurpinar, C.[Cemal], Takir, S.[Seyma], Bicer, E.[Erhan], Uluer, P.[Pinar], Arica, N.[Nafiz], Kose, H.[Hatice],
Contrastive learning based facial action unit detection in children with hearing impairment for a socially assistive robot platform,
IVC(128), 2022, pp. 104572.
Elsevier DOI 2212
Contrastive learning, Facial action unit detection, Child-robot interaction, Transfer learning, Domain adaptation, Covariate shift BibRef

Song, W.Y.[Wen-Yu], Shi, S.[Shuze], Dong, Y.[Yu], An, G.[Gaoyun],
Heterogeneous spatio-temporal relation learning network for facial action unit detection,
PRL(164), 2022, pp. 268-275.
Elsevier DOI 2212
Facial action unit, Spatio-temporal relation, Graph neural network, Transformer BibRef

Ntinou, I.[Ioanna], Sanchez, E.[Enrique], Bulat, A.[Adrian], Valstar, M.[Michel], Tzimiropoulos, G.[Georgios],
A Transfer Learning Approach to Heatmap Regression for Action Unit Intensity Estimation,
AffCom(14), No. 1, January 2023, pp. 436-450.
IEEE DOI 2303
Gold, Task analysis, Heating systems, Correlation, Transfer learning, Estimation, Heat transfer, transfer learning BibRef

Kumar, H.N.N.[H.N. Naveen], Kumar, A.S.[A Suresh], Prasad, M.S.G.[M.S. Guru], Shah, M.A.[Mohd Asif],
Automatic facial expression recognition combining texture and shape features from prominent facial regions,
IET-IPR(17), No. 4, 2023, pp. 1111-1125.
DOI Link 2303
automatic facial expression recognition (AFER), facial local regions, generalization capability, shape and texture feature fusion BibRef

Ouafa, C.[Chebah], Tayeb, L.M.[Laskri Mohamed],
Local directional double ternary coding pattern for facial expression recognition,
IJCVR(13), No. 3, 2023, pp. 259-284.
DOI Link 2305
BibRef

Fan, Y.[Yingruo], Lam, J.C.K.[Jacqueline C.K.], Li, V.O.K.[Victor O.K.],
Distilling Region-Wise and Channel-Wise Deep Structural Facial Relationships for FAU (DSR-FAU) Intensity Estimation,
AffCom(14), No. 2, April 2023, pp. 986-997.
IEEE DOI 2306
Estimation, Computational modeling, Heating systems, Feature extraction, Knowledge engineering, Correlation, Training, teacher-student framework BibRef

Yan, J.W.[Jing-Wei], Wang, J.J.[Jing-Jing], Li, Q.[Qiang], Wang, C.[Chunmao], Pu, S.L.[Shi-Liang],
Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition,
MultMed(25), 2023, pp. 1760-1772.
IEEE DOI 2306
Gold, Task analysis, Face recognition, Feature extraction, Representation learning, Optical imaging, Facial muscles, weakly supervised learning BibRef

Shang, Z.Q.[Zi-Qiao], Du, C.J.[Cong-Ju], Li, B.Y.[Bing-Yin], Yan, Z.Q.[Zeng-Qiang], Yu, L.[Li],
MMA-Net: Multi-view mixed attention mechanism for facial action unit detection,
PRL(172), 2023, pp. 165-171.
Elsevier DOI 2309
AU Detection, Multi-view partitioning scheme, Mixed attention mechanism, Cross-view contrastive loss BibRef

Chen, H.F.[Hai-Feng], Jiang, D.M.[Dong-Mei], Zhao, Y.[Yong], Wei, X.Y.[Xiao-Yong], Lu, K.[Ke], Sahli, H.[Hichem],
Region Attentive Action Unit Intensity Estimation With Uncertainty Weighted Multi-Task Learning,
AffCom(14), No. 3, July 2023, pp. 2033-2047.
IEEE DOI 2310
BibRef

Yang, J.[Jing], Hristov, Y.[Yordan], Shen, J.[Jie], Lin, Y.M.[Yi-Ming], Pantic, M.[Maja],
Toward Robust Facial Action Units' Detection,
PIEEE(111), No. 10, October 2023, pp. 1198-1214.
IEEE DOI Code:
WWW Link. 2310
BibRef

Churamani, N.[Nikhil], Kara, O.[Ozgur], Gunes, H.[Hatice],
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition,
AffCom(14), No. 4, October 2023, pp. 3191-3206.
IEEE DOI 2312
BibRef

Tallec, G.[Gauthier], Dapogny, A.[Arnaud], Bailly, K.[Kévin],
Multi-Order Networks for Action Unit Detection,
AffCom(14), No. 4, October 2023, pp. 2876-2888.
IEEE DOI 2312
BibRef

Zhang, W.[Wei], Li, L.[Lincheng], Ding, Y.[Yu], Chen, W.[Wei], Deng, Z.G.[Zhi-Gang], Yu, X.[Xin],
Detecting Facial Action Units From Global-Local Fine-Grained Expressions,
CirSysVideo(34), No. 2, February 2024, pp. 983-994.
IEEE DOI 2402
Gold, Feature extraction, Face recognition, Faces, Training, Transformers, Action units, facial expression, deep learning BibRef

Shao, Z.W.[Zhi-Wen], Zhou, Y.[Yong], Li, F.[Feiran], Zhu, H.C.[Han-Cheng], Liu, B.[Bing],
Joint facial action unit recognition and self-supervised optical flow estimation,
PRL(181), 2024, pp. 70-76.
Elsevier DOI 2405
Joint framework, Facial AU recognition, Self-supervised optical flow estimation, Identical mapping constraint BibRef

Hinduja, S.[Saurabh], Nourivandi, T.[Tara], Cohn, J.F.[Jeffrey F.], Canavan, S.[Shaun],
Time to retire F1-binary score for action unit detection,
PRL(182), 2024, pp. 111-117.
Elsevier DOI 2405
Action units, Data imbalance, Machine learning, F1 score BibRef

Tellamekala, M.K.[Mani Kumar], Sümer, Ö.[Ömer], Schuller, B.W.[Björn W.], André, E.[Elisabeth], Giesbrecht, T.[Timo], Valstar, M.[Michel],
Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?,
AffCom(15), No. 2, April 2024, pp. 535-548.
IEEE DOI 2406
Face recognition, Shape, Solid modeling, Gold, Task analysis, Computational modeling, Facial expression analysis, 3D morphable models BibRef

Li, X.T.[Xiao-Tian], Zhang, Z.[Zheng], Zhang, X.[Xiang], Wang, T.[Taoyue], Li, Z.H.[Zhi-Hua], Yang, H.Y.[Hui-Yuan], Ciftci, U.[Umur], Ji, Q.[Qiang], Cohn, J.[Jeffrey], Yin, L.J.[Li-Jun],
Disagreement Matters: Exploring Internal Diversification for Redundant Attention in Generic Facial Action Analysis,
AffCom(15), No. 2, April 2024, pp. 620-631.
IEEE DOI 2406
Gold, Transformers, Task analysis, Face recognition, Redundancy, Feature extraction, Databases, Attention, disagreement, diversity, transformer BibRef

Ma, B.[Bowen], An, R.[Rudong], Zhang, W.[Wei], Ding, Y.[Yu], Zhao, Z.[Zeng], Zhang, R.S.[Rong-Sheng], Lv, T.J.[Tang-Jie], Fan, C.J.[Chang-Jie], Hu, Z.P.[Zhi-Peng],
Facial Action Unit Detection and Intensity Estimation From Self-Supervised Representation,
AffCom(15), No. 3, July 2024, pp. 1669-1683.
IEEE DOI 2409
Gold, Estimation, Image reconstruction, Annotations, Face recognition, Task analysis, Visualization, Facial action unit, self-supervised pre-training BibRef

Chen, H.F.[Hai-Feng], Zhang, P.[Peng], Guo, C.[Chujia], Lu, K.[Ke], Jiang, D.M.[Dong-Mei],
Facial Action Unit Representation Based on Self-Supervised Learning With Ensembled Priori Constraints,
IP(33), 2024, pp. 5045-5059.
IEEE DOI Code:
WWW Link. 2410
Faces, Gold, Face recognition, Videos, Image reconstruction, Image color analysis, Lighting, AU representation, average face BibRef

Yu, J.[Jun], Wei, Z.H.[Zhi-Hong], Cai, Z.[Zhongpeng], Zhao, G.P.[Gong-Peng], Zhang, Z.[Zerui], Wang, Y.Q.[Yong-Qi], Xie, G.[Guochen], Zhu, J.[Jichao], Zhu, W.[Wangyuan], Liu, Q.S.[Qing-Song], Liang, J.[Jiaen],
Exploring Facial Expression Recognition through Semi-Supervised Pre-training and Temporal Modeling,
ABAW24(4880-4887)
IEEE DOI 2410
Image recognition, Accuracy, Face recognition, Genetic expression, Semisupervised learning, Facial Expression Recognition, Temporal Modeling BibRef

Jin, S.W.[Shi-Wei], Wang, Z.[Zhen], Wang, L.[Lei], Liu, P.[Peng], Bi, N.[Ning], Nguyen, T.[Truong],
AUEditNet: Dual-Branch Facial Action Unit Intensity Manipulation with Implicit Disentanglement,
CVPR24(2104-2113)
IEEE DOI 2410
Gold, Accuracy, Annotations, Face recognition, Pipelines, Estimation BibRef

Yu, J.[Jun], Zhang, Z.[Zerui], Wei, Z.H.[Zhi-Hong], Zhao, G.[Gongpeng], Cai, Z.[Zhongpeng], Wang, Y.Q.[Yong-Qi], Xie, G.[Guochen], Zhu, J.[Jichao], Zhu, W.[Wangyuan], Liu, Q.S.[Qing-Song], Liang, J.[Jiaen],
AUD-TGN: Advancing Action Unit Detection with Temporal Convolution and GPT-2 in Wild Audiovisual Contexts,
ABAW24(4814-4821)
IEEE DOI 2410
Visualization, Gold, Accuracy, Feature extraction, Data models, Robustness, Convolutional neural networks BibRef

Savchenko, A.V.[Andrey V.], Sidorova, A.P.[Anna P.],
EmotiEffNet and Temporal Convolutional Networks in Video-based Facial Expression Recognition and Action Unit Detection,
ABAW24(4849-4859)
IEEE DOI 2410
Measurement, Gold, Visualization, Face recognition, Transformers, Feature extraction, Facial expression recognition, HSEmotion library BibRef

Wang, Z.[Zihan], Song, S.Y.[Si-Yang], Luo, C.[Cheng], Deng, S.[Songhe], Xie, W.C.[Wei-Cheng], Shen, L.L.[Lin-Lin],
Multi-Scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition,
CVPR24(1270-1280)
IEEE DOI Code:
WWW Link. 2410
Gold, Adaptation models, Graphical models, Codes, Adaptive systems, Computational modeling BibRef

Belharbi, S.[Soufiane], Pedersoli, M.[Marco], Koerich, A.L.[Alessandro Lameiras], Bacon, S.[Simon], Granger, E.[Eric],
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues,
FG24(1-10)
IEEE DOI Code:
WWW Link. 2408
Training, Heating systems, Location awareness, Gold, Visualization, Codes, Face recognition BibRef

Yin, Y.F.[Yu-Feng], Chang, D.[Di], Song, G.X.[Guo-Xian], Sang, S.[Shen], Zhi, T.C.[Tian-Cheng], Liu, J.[Jing], Luo, L.J.[Lin-Jie], Soleymani, M.[Mohammad],
FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features,
WACV24(6087-6096)
IEEE DOI Code:
WWW Link. 2404
Training, Heating systems, Gold, Costs, Codes, Benchmark testing, Algorithms, Biometrics, face, gesture, body pose BibRef

Zhang, X.[Xiang], Yang, H.Y.[Hui-Yuan], Wang, T.[Taoyue], Li, X.T.[Xiao-Tian], Yin, L.J.[Li-Jun],
Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection,
WACV24(6065-6074)
IEEE DOI 2404
Representation learning, Gold, Redundancy, Feature extraction, Robustness, Data models, Algorithms, Biometrics, face, gesture, Image recognition and understanding BibRef

Li, X.T.[Xiao-Tian], Wang, T.[Taoyue], Zhao, G.[Geran], Zhang, X.[Xiang], Kang, X.[Xi], Yin, L.J.[Li-Jun],
ReactioNet: Learning High-order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning,
ICCV23(20717-20728)
IEEE DOI 2401
BibRef

Li, X.T.[Xiao-Tian], Zhang, X.[Xiang], Wang, T.[Taoyue], Yin, L.J.[Li-Jun],
Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity,
ICCV23(20922-20932)
IEEE DOI 2401
BibRef

Li, X.[Ximan], Deng, W.H.[Wei-Hong], Li, S.[Shan], Li, Y.[Yong],
Compound Expression Recognition In-the-wild with AU-assisted Meta Multi-task Learning,
ABAW23(5735-5744)
IEEE DOI 2309
BibRef

Savchenko, A.V.[Andrey V.],
EmotiEffNets for Facial Processing in Video-based Valence-Arousal Prediction, Expression Classification and Action Unit Detection,
ABAW23(5716-5724)
IEEE DOI 2309
BibRef

Vu, N.T.[Ngoc Tu], Huynh, V.T.[Van Thong], Nguyen, T.N.[Trong Nghia], Kim, S.H.[Soo-Hyung],
Ensemble Spatial and Temporal Vision Transformer for Action Units Detection,
ABAW23(5770-5776)
IEEE DOI 2309
BibRef

Yu, J.[Jun], Li, R.[Renda], Cai, Z.[Zhongpeng], Zhao, G.[Gongpeng], Xie, G.[Guochen], Zhu, J.[Jichao], Zhu, W.Y.[Wang-Yuan], Ling, Q.[Qiang], Wang, L.[Lei], Wang, C.[Cong], Qiu, L.[Luyu], Zheng, W.[Wei],
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection,
ABAW23(5785-5792)
IEEE DOI 2309
BibRef

Deng, Y.Y.[Yuan-Yuan], Liu, X.L.[Xiao-Long], Meng, L.[Liyu], Jiang, W.Q.[Wen-Qiang], Dong, Y.Q.[You-Qiang], Liu, C.[Chuanhe],
Multi-modal Information Fusion for Action Unit Detection in the Wild,
ABAW23(5855-5862)
IEEE DOI 2309
BibRef

Wang, Z.H.[Zi-Han], Song, S.Y.[Si-Yang], Luo, C.[Cheng], Zhou, Y.Z.[Yu-Zhi], Wu, S.[Shiling], Xie, W.C.[Wei-Cheng], Shen, L.L.[Lin-Lin],
Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection,
ABAW23(5899-5907)
IEEE DOI 2309
BibRef

Cui, Z.J.[Zi-Jun], Kuang, C.Y.[Chen-Yi], Gao, T.[Tian], Talamadupula, K.[Kartik], Ji, Q.[Qiang],
Biomechanics-Guided Facial Action Unit Detection Through Force Modeling,
CVPR23(8694-8703)
IEEE DOI 2309
BibRef

Nguyen, D.K.[Dang-Khanh], Pant, S.[Sudarshan], Ho, N.H.[Ngoc-Huynh], Lee, G.S.[Guee-Sang], Kim, S.H.[Soo-Hyung], Yang, H.J.[Hyung-Jeong],
Affective Behavior Analysis Using Action Unit Relation Graph and Multi-task Cross Attention,
ABAWE22(132-142).
Springer DOI 2304
BibRef

Zhang, X.[Xiang], Yin, L.J.[Li-Jun],
Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers,
FG21(1-8)
IEEE DOI 2303
Representation learning, Databases, Fuses, Semantics, Gesture recognition, Transformers BibRef

Kawamura, R.[Ryosuke], Murase, K.[Kentaro],
Facial Action Unit Detection Based on Teacher-Student Learning Framework for Partially Occluded Facial Images,
FG21(01-05)
IEEE DOI 2303
Databases, Face recognition, Glass, Gesture recognition, Feature extraction BibRef

Li, X.T.[Xiao-Tian], Li, Z.H.[Zhi-Hua], Yang, H.Y.[Hui-Yuan], Zhao, G.[Geran], Yin, L.J.[Li-Jun],
Your 'Attention' Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis,
FG21(01-08)
IEEE DOI 2303
Representation learning, Visualization, Correlation, Uncertainty, Databases, Face recognition BibRef

Ge, X.[Xuri], Wan, P.C.[Peng-Cheng], Han, H.[Hu], Jose, J.M.[Joemon M.], Ji, Z.L.[Zhi-Long], Wu, Z.Q.[Zhong-Qin], Liu, X.[Xiao],
Local Global Relational Network for Facial Action Units Recognition,
FG21(01-08)
IEEE DOI 2303
Shape, Face recognition, Gesture recognition, Benchmark testing, Feature extraction BibRef

Yin, Y.F.[Yu-Feng], Lu, L.[Liupei], Wu, Y.Z.[Yi-Zhen], Soleymani, M.[Mohammad],
Self-Supervised Patch Localization for Cross-Domain Facial Action Unit Detection,
FG21(1-8)
IEEE DOI 2303
Location awareness, Solid modeling, Face recognition, Training data, Computer architecture BibRef

Saito, J.Y.[Jun-Ya], Yamamoto, T.[Takahisa], Uchida, A.[Akiyoshi], Mi, X.Y.[Xiao-Yu], Murase, K.[Kentaro],
Facial Action Unit Recognition Using Pseudo-Intensities and their Transformation,
FG21(01-08)
IEEE DOI 2303
Correlation coefficient, Face recognition, Education, Transforms, Medical services BibRef

Yang, J.[Jing], Shen, J.[Jie], Lin, Y.M.[Yi-Ming], Hristov, Y.[Yordan], Pantic, M.[Maja],
FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection,
WACV23(6008-6016)
IEEE DOI 2302
Training, Gold, Correlation, Convolution, Computational modeling, Transformers, Algorithms: Biometrics, face, gesture, body pose BibRef

Liu, Y.[Yang], Zhang, X.M.[Xing-Ming], Kauttonen, J.[Janne], Zhao, G.Y.[Guo-Ying],
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition,
ICPR22(777-783)
IEEE DOI 2212
Training, Gold, Image recognition, Uncertainty, Annotations, Face recognition, Semantics BibRef

Hoai, D.L.[Duy Le], Lim, E.[Eunchae], Choi, E.[Eunbin], Kim, S.[Sieun], Pant, S.[Sudarshan], Lee, G.S.[Guee-Sang], Kim, S.H.[Soo-Huyng], Yang, H.J.[Hyung-Jeong],
An Attention-based Method for Multi-label Facial Action Unit Detection,
ABAW22(2453-2458)
IEEE DOI 2210
Human computer interaction, Gold, Computational modeling, Feature extraction, Encoding BibRef

Wang, L.F.[Ling-Feng], Qi, J.[Jin], Cheng, J.[Jian], Suzuki, K.[Kenji],
Action unit detection by exploiting spatial-temporal and label-wise attention with transformer,
ABAW22(2469-2474)
IEEE DOI 2210
Visualization, Gold, Correlation, Convolution, Computational modeling, Predictive models, Transformers BibRef

Kollias, D.[Dimitrios],
Multi-Label Compound Expression Recognition: C-EXPR Database and Network,
CVPR23(5589-5598)
IEEE DOI 2309
BibRef

Kollias, D.[Dimitrios], Tzirakis, P.[Panagiotis], Baird, A.[Alice], Cowen, A.[Alan], Zafeiriou, S.[Stefanos],
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection and Emotional Reaction Intensity Estimation Challenges,
ABAW23(5889-5898)
IEEE DOI 2309
BibRef

Kollias, D.[Dimitrios],
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges,
ABAW22(2327-2335)
IEEE DOI 2210
Measurement, Databases, Estimation, Benchmark testing, Multitasking BibRef

Jiang, W.Q.[Wen-Qiang], Wu, Y.N.[Yan-Nan], Qiao, F.S.[Feng-Sheng], Meng, L.[Liyu], Deng, Y.Y.[Yuan-Yuan], Liu, C.H.[Chuan-He],
Model Level Ensemble for Facial Action Unit Recognition at the 3rd ABAW Challenge,
ABAW22(2336-2343)
IEEE DOI 2210
Gold, Face recognition, Feature extraction, Behavioral sciences, Task analysis BibRef

Tang, Y.[Yang], Zeng, W.D.[Wang-Ding], Zhao, D.F.[Da-Fei], Zhang, H.G.[Hong-Gang],
PIAP-DF: Pixel-Interested and Anti Person-Specific Facial Action Unit Detection Net with Discrete Feedback Learning,
ICCV21(12879-12888)
IEEE DOI 2203
Training, Gold, Computational modeling, Learning automata, Psychology, Production, Semisupervised learning, Recognition and classification BibRef

Bishay, M.[Mina], Ghoneim, A.[Ahmed], Ashraf, M.[Mohamed], Mavadati, M.[Mohammad],
Choose Settings Carefully: Comparing Action Unit Detection At Different Settings Using A Large-Scale Dataset,
ICIP21(2883-2887)
IEEE DOI 2201
Training, Gold, Image resolution, Image color analysis, Gray-scale, Data models, AU detection, CNNs, Preprocessing settings, Training set size BibRef

Jampour, M.[Mahdi], Sardar, A.K.[Amin Karimi],
Facial Expression Recognition using Multi-Feature Concatenation of Local Face Components and Hierarchical SVM,
IPRIA21(1-6)
IEEE DOI 2201
Support vector machines, Histograms, Image recognition, Image analysis, Face recognition, Mouth, Lighting, Hierarchical SVM BibRef

Jin, Y.[Yue], Zheng, T.Q.[Tian-Qing], Gao, C.[Chao], Xu, G.Q.[Guo-Qiang],
MTMSN: Multi-Task and Multi-Modal Sequence Network for Facial Action Unit and Expression Recognition,
ABAW21(3590-3595)
IEEE DOI 2112
Gold, Visualization, Annotations, Face recognition, Computational modeling, Conferences BibRef

Jacob, G.M.[Geethu Miriam], Stenger, B.[Björn],
Facial Action Unit Detection With Transformers,
CVPR21(7676-7685)
IEEE DOI 2111
Correlation, Face recognition, Taxonomy, Training data, Computer architecture, Muscles, Transformers BibRef

Yang, H.Y.[Hui-Yuan], Yin, L.J.[Li-Jun], Zhou, Y.[Yi], Gu, J.X.[Jiu-Xiang],
Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection,
CVPR21(10477-10486)
IEEE DOI 2111
Gold, Visualization, Databases, Semantics, Feature extraction BibRef

Song, T.F.[Teng-Fei], Cui, Z.J.[Zi-Jun], Zheng, W.M.[Wen-Ming], Ji, Q.[Qiang],
Hybrid Message Passing with Performance-Driven Structures for Facial Action Unit Detection,
CVPR21(6263-6272)
IEEE DOI 2111
Gold, Monte Carlo methods, Databases, Message passing, Neural networks, Markov processes BibRef

Tang, Y.[Yang], Chen, S.[Shuang], Zhang, H.G.[Hong-Gang], Wang, G.[Gang], Yang, R.[Rui],
MRP-Net: A Light Multiple Region Perception Neural Network for Multi-label AU Detection,
ICPR21(1844-1851)
IEEE DOI 2105
Performance evaluation, Learning systems, Deep learning, Gold, Neural networks, Psychology, Feature extraction BibRef

Li, Z.H.[Zhi-Hua], Zhang, Z.[Zheng], Yin, L.J.[Li-Jun],
SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection,
ICPR21(5036-5043)
IEEE DOI 2105
Training, Correlation, Databases, Fuses, Knowledge based systems, Focusing BibRef

Sanchez, E.[Enrique], Bulat, A.[Adrian], Zaganidis, A.[Anestis], Tzimiropoulos, G.[Georgios],
Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning,
ACCV20(V:104-120).
Springer DOI 2103
BibRef

Yang, H.Y.[Hui-Yuan], Yin, L.J.[Li-Jun],
Re-net: A Relation Embedded Deep Model for AU Occurrence and Intensity Estimation,
ACCV20(V:137-153).
Springer DOI 2103
BibRef

Hinduja, S., Canavan, S.,
Real-time Action Unit Intensity Detection,
FG20(916-916)
IEEE DOI 2102
Gold, Real-time systems, Training data, Neurons, Machine learning, Face detection, Convolutional neural networks BibRef

Yang, H., Wang, T., Yin, L.,
Set Operation Aided Network for Action Units Detection,
FG20(229-235)
IEEE DOI 2102
Feature extraction, Gold, Training, Hidden Markov models, Data models, Task analysis, Face recognition, deep neural networks BibRef

Rieger, I.[Ines], Pahl, J.[Jaspar], Finzel, B.[Bettina], Schmid, U.[Ute],
CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition,
ICPR22(798-804)
IEEE DOI 2212
Knowledge engineering, Training, Face recognition, Neural networks, Testing BibRef

Rieger, I.[Ines], Pahl, J.[Jaspar], Seuss, D.,
Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection,
FG20(619-623)
IEEE DOI 2102
Training, Face recognition, Videos, Testing, Optimization, Gold, Task analysis, Multi label, FACS, Multi label Balancing, Deep Learning BibRef

Wörtwein, T., Morency, L.P.,
Simple and Effective Approaches for Uncertainty Prediction in Facial Action Unit Intensity Regression,
FG20(452-456)
IEEE DOI 2102
Uncertainty, Gold, Predictive models, Estimation, Training, Task analysis, Computational modeling BibRef

Corneanu, C., Madadi, M., Escalera, S., Martinez, A.,
Explainable Early Stopping for Action Unit Recognition,
FG20(693-699)
IEEE DOI 2102
Face recognition, Training, Gold, Topology, Network topology, Standards, Monitoring, deep learning, facial AU recognition, explainable deep learning BibRef

Ji, S., Wang, K., Peng, X., Yang, J., Zeng, Z., Qiao, Y.,
Multiple Transfer Learning and Multi-label Balanced Training Strategies for Facial AU Detection In the Wild,
EmotioNet20(1657-1661)
IEEE DOI 2008
Task analysis, Feature extraction, Face recognition, Training, Mathematical model, Muscles BibRef

Werner, P., Saxen, F., Al-Hamadi, A.,
Facial Action Unit Recognition in the Wild with Multi-Task CNN Self-Training for the EmotioNet Challenge,
EmotioNet20(1649-1652)
IEEE DOI 2008
Training, Head, Manuals, Optimized production technology, Neurons BibRef

Liu, Z.L.[Zhi-Lei], Liu, D.Y.[Di-Yi], Wu, Y.P.[Yun-Peng],
Region Based Adversarial Synthesis of Facial Action Units,
MMMod20(II:514-526).
Springer DOI 2003
BibRef

Liu, Z.L.[Zhi-Lei], Dong, J.H.[Jia-Hui], Zhang, C.C.[Cui-Cui], Wang, L.B.[Long-Biao], Dang, J.W.[Jian-Wu],
Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection,
MMMod20(II:489-501).
Springer DOI 2003
BibRef

Hong, J.[Joanna], Lee, H.J.[Hong Joo], Kim, Y.[Yelin], Ro, Y.M.[Yong Man],
Face Tells Detailed Expression: Generating Comprehensive Facial Expression Sentence Through Facial Action Units,
MMMod20(II:100-111).
Springer DOI 2003
BibRef

Zhang, Y.[Yong], Wu, B.Y.[Bao-Yuan], Dong, W.M.[Wei-Ming], Li, Z.F.[Zhi-Feng], Liu, W.[Wei], Hu, B.G.[Bao-Gang], Ji, Q.A.[Qi-Ang],
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation,
CVPR19(3452-3461).
IEEE DOI 2002
BibRef

Niu, X.S.[Xue-Song], Han, H.[Hu], Yang, S.F.[Song-Fan], Huang, Y.[Yan], Shan, S.G.[Shi-Guang],
Local Relationship Learning With Person-Specific Shape Regularization for Facial Action Unit Detection,
CVPR19(11909-11918).
IEEE DOI 2002
BibRef

Liu, P., Zhang, Z., Yang, H., Yin, L.,
Multi-Modality Empowered Network for Facial Action Unit Detection,
WACV19(2175-2184)
IEEE DOI 1904
face recognition, image colour analysis, image reconstruction, infrared imaging, learning (artificial intelligence), Color BibRef

Mei, C., Jiang, F., Shen, R., Hu, Q.,
Region and Temporal Dependency Fusion for Multi-label Action Unit Detection,
ICPR18(848-853)
IEEE DOI 1812
Gold, Feature extraction, Videos, Logic gates, Face, Correlation, Fuses BibRef

Zhang, Y., Dong, W., Hu, B., Ji, Q.,
Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation,
CVPR18(2314-2323)
IEEE DOI 1812
Gold, Estimation, Training, Face, Image segmentation, Neural networks BibRef

Song, T.F.[Teng-Fei], Cui, Z.J.[Zi-Jun], Wang, Y.R.[Yu-Ru], Zheng, W.M.[Wen-Ming], Ji, Q.[Qiang],
Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation,
CVPR21(4843-4852)
IEEE DOI 2111
Convolution, Computational modeling, Semantics, Estimation, Probabilistic logic, Feature extraction BibRef

Zhang, Y.[Yong], Zhao, R.[Rui], Dong, W.M.[Wei-Ming], Hu, B.G.[Bao-Gang], Ji, Q.[Qiang],
Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation,
CVPR18(7034-7043)
IEEE DOI 1812
Estimation, Data models, Databases, Training, Image segmentation, Pattern recognition BibRef

Ertugrul, I.O., Jeni, L.A., Cohn, J.F.,
FACSCaps: Pose-Independent Facial Action Coding with Capsules,
AMFG18(2211-221109)
IEEE DOI 1812
Gold, Image reconstruction, Computer architecture, Face, Training, Feature extraction, Routing BibRef

Han, S., Meng, Z., Li, Z., O'Reilly, J., Cai, J., Wang, X., Tong, Y.,
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition,
CVPR18(5070-5078)
IEEE DOI 1812
Convolution, Face recognition, Feature extraction, Interpolation, Gold, Training, Databases BibRef

Corneanu, C.[Ciprian], Madadi, M.[Meysam], Escalera, S.[Sergio],
Deep Structure Inference Network for Facial Action Unit Recognition,
ECCV18(XII: 309-324).
Springer DOI 1810
BibRef

Albiero, V., Bellon, O.R.P., Silva, L.,
Multi-Label Action Unit Detection on Multiple Head Poses with Dynamic Region Learning,
ICIP18(2037-2041)
IEEE DOI 1809
Databases, Gold, Face, Training, Feature extraction, Robustness, Facial expression analysis, action unit detection, deep learning, transfer-learning BibRef

Li, L., Baltrusaitis, T., Sun, B., Morency, L.P.,
Edge Convolutional Network for Facial Action Intensity Estimation,
FG18(171-178)
IEEE DOI 1806
Detectors, Estimation, Generators, Gold, Image edge detection, Kernel, Logic gates, Conolutional network, Facial action unit, Facial expression BibRef

Dhamija, S., Boult, T.E.,
Automated Action Units Vs. Expert Raters: Face off,
WACV18(259-268)
IEEE DOI 1806
crowdsourcing, human computer interaction, learning (artificial intelligence), Automated Action Units, Training BibRef

Tran, D.L., Walecki, R., Rudovic, O., Eleftheriadis, S., Schuller, B., Pantic, M.,
DeepCoder: Semi-Parametric Variational Autoencoders for Automatic Facial Action Coding,
ICCV17(3209-3218)
IEEE DOI 1802
Gaussian processes, emotion recognition, face recognition, feature extraction, image representation, Image reconstruction BibRef

Chang, W.Y., Hsu, S.H., Chien, J.H.,
FATAUVA-Net: An Integrated Deep Learning Framework for Facial Attribute Recognition, Action Unit Detection, and Valence-Arousal Estimation,
FaceWild17(1963-1971)
IEEE DOI 1709
Emotion recognition, Estimation, Face, Face recognition, Feature extraction, Gold, Machine, learning BibRef

Batista, J.C., Albiero, V., Bellon, O.R.P., Silva, L.,
AUMPNet: Simultaneous Action Units Detection and Intensity Estimation on Multipose Facial Images Using a Single Convolutional Neural Network,
FG17(866-871)
IEEE DOI 1707
Databases, Estimation, Face, Gold, Magnetic heads, Optimization BibRef

Zhou, Y., Pi, J., Shi, B.E.,
Pose-Independent Facial Action Unit Intensity Regression Based on Multi-Task Deep Transfer Learning,
FG17(872-877)
IEEE DOI 1707
Estimation, Face, Face recognition, Gold, Hidden Markov models, Neural networks, Training BibRef

Tang, C.G.[Chuan-Gao], Zheng, W.M.[Wen-Ming], Yan, J.W.[Jing-Wei], Li, Q.[Qiang], Li, Y.[Yang], Zhang, T.[Tong], Cui, Z.[Zhen],
View-Independent Facial Action Unit Detection,
FG17(878-882)
IEEE DOI 1707
Face, Feature extraction, Gold, Image segmentation, Lips, Neural networks, Training BibRef

He, J., Li, D., Yang, B., Cao, S., Sun, B., Yu, L.,
Multi View Facial Action Unit Detection Based on CNN and BLSTM-RNN,
FG17(848-853)
IEEE DOI 1707
Face, Face recognition, Feature extraction, Gold, Kernel, Machine learning, Training BibRef

Li, X., Chen, S., Jin, Q.,
Facial Action Units Detection with Multi-Features and -AUs Fusion,
FG17(860-865)
IEEE DOI 1707
Face, Face recognition, Feature extraction, Gold, Radio frequency, Support vector machines, Training BibRef

Amirian, M., Kächele, M.[Markus], Palm, G., Schwenker, F.[Friedhelm],
Support Vector Regression of Sparse Dictionary-Based Features for View-Independent Action Unit Intensity Estimation,
FG17(854-859)
IEEE DOI 1707
Dictionaries, Encoding, Estimation, Face, Gold, Matching pursuit algorithms, Shape BibRef

Kachele, M.[Markus], Schwenker, F.[Friedhelm],
Cascaded Fusion of Dynamic, Spatial, and Textural Feature Sets for Person-Independent Facial Emotion Recognition,
ICPR14(4660-4665)
IEEE DOI 1412
Computer architecture BibRef

Kim, E., Vangala, S.,
Deep Action Unit classification using a binned intensity loss and semantic context model,
ICPR16(4136-4141)
IEEE DOI 1705
Convolutional codes, Entropy, Feature extraction, Gold, Neural networks, Semantics, Training BibRef

Tosér, Z.[Zoltán], Jeni, L.A.[László A.], Lorincz, A.[András], Cohn, J.F.[Jeffrey F.],
Deep Learning for Facial Action Unit Detection Under Large Head Poses,
ChaLearn16(III: 359-371).
Springer DOI 1611
BibRef

Jaiswal, S., Valstar, M.F.,
Deep learning the dynamic appearance and shape of facial action units,
WACV16(1-8)
IEEE DOI 1606
Computer architecture BibRef

Werner, P.[Philipp], Saxen, F.[Frerk], Al-Hamadi, A.[Ayoub],
Handling Data Imbalance in Automatic Facial Action Intensity Estimation,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Ma, S.[Shuang], McDuff, D.J.[Daniel J.], Song, Y.[Yale],
Unpaired Image-to-Speech Synthesis With Multimodal Information Bottleneck,
ICCV19(7597-7606)
IEEE DOI 2004
image processing, learning (artificial intelligence), speech synthesis, text analysis, image-to-speech synthesis, Transforms BibRef

Song, Y., McDuff, D.J., Vasisht, D., Kapoor, A.,
Exploiting sparsity and co-occurrence structure for action unit recognition,
FG15(1-8)
IEEE DOI 1508
Bayes methods BibRef

Chen, J.[Junkai], Chen, Z.[Zenghai], Chi, Z.[Zheru], Fu, H.[Hong],
Recognition of Facial Action Units with Action Unit Classifiers and an Association Network,
CV4AC14(672-683).
Springer DOI 1504
BibRef

Bingol, D.[Deniz], Celik, T.[Turgay], Omlin, C.W.[Christian W.], Vadapalli, H.B.[Hima B.],
Facial action unit intensity estimation using rotation invariant features and regression analysis,
ICIP14(1381-1385)
IEEE DOI 1502
BibRef

Han, S.Z.[Shi-Zhong], Meng, Z.[Zibo], Liu, P.[Ping], Tong, Y.[Yan],
Facial grid transformation: A novel face registration approach for improving facial action unit recognition,
ICIP14(1415-1419)
IEEE DOI 1502
Databases BibRef

Zhang, X.[Xiao], Mahoor, M.H.[Mohammad H.],
Simultaneous Detection of Multiple Facial Action Units via Hierarchical Task Structure Learning,
ICPR14(1863-1868)
IEEE DOI 1412
Databases BibRef

Jiang, B.[Bihan], Martinez, B.[Brais], Pantic, M.[Maja],
Parametric temporal alignment for the detection of facial action temporal segments,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Khademi, M.[Mahmoud], Morency, L.P.[Louis-Philippe],
Relative facial action unit detection,
WACV14(1090-1095)
IEEE DOI 1406
Databases BibRef

Hsu, G.S.[Gee-Sern], Yeh, S.M.[Shang-Min],
Heterogeneous feature code for expression recognition,
ICIP13(2407-2411)
IEEE DOI 1402
Expression recognition;facial features;feature extraction BibRef

Yuce, A., Sorci, M., Thiran, J.P.,
Improved local binary pattern based action unit detection using morphological and bilateral filters,
FG13(1-7)
IEEE DOI 1309
face recognition BibRef

Yang, S.F.[Song-Fan], An, L.[Le], Bhanu, B., Thakoor, N.,
Improving action units recognition using dense flow-based face registration in video,
FG13(1-8)
IEEE DOI 1309
affine transforms BibRef

Liu, M.Y.[Meng-Yi], Li, S.X.[Shao-Xin], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
AU-aware Deep Networks for facial expression recognition,
FG13(1-6)
IEEE DOI 1309
Boltzmann machines BibRef

Mavadati, S.M.[S. Mohammad], Mahoor, M.H.[Mohammad H.], Bartlett, K.[Kevin], Trinh, P.[Philip],
Automatic detection of non-posed facial action units,
ICIP12(1817-1820).
IEEE DOI 1302
BibRef

Selpi, Wilhelm, T.[Torsten], Jansson, M.[Marcus], Hagstrom, L.[Li], Brandin, N.[Niklas], Andersson, M.[Magnus], Gronvall, J.F.[John-Fredrik],
Automatic real-time FACS-coder to anonymise drivers in eye tracker videos,
CVVT11(1986-1993).
IEEE DOI 1201
BibRef

Cosker, D.[Darren], Krumhuber, E.[Eva], Hilton, A.[Adrian],
A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling,
ICCV11(2296-2303).
IEEE DOI 1201
BibRef

Gehrig, T.[Tobias], Al-Halah, Z., Ekenel, H.K.[Hazim Kemal], Stiefelhagen, R.,
Action unit intensity estimation using hierarchical partial least squares,
FG15(1-6)
IEEE DOI 1508
Gaussian processes BibRef

Gehrig, T.[Tobias], Ekenel, H.K.[Hazim Kemal],
Facial action unit detection using kernel partial least squares,
BenchFace11(2092-2099).
IEEE DOI 1201
BibRef
And:
A common framework for real-time emotion recognition and facial action unit detection,
CVPR4HB11(1-6).
IEEE DOI 1106
BibRef

Jiang, B.[Bihan], Martinez, B.[Brais], Valstar, M.F.[Michel F.], Pantic, M.[Maja],
Decision Level Fusion of Domain Specific Regions for Facial Action Recognition,
ICPR14(1776-1781)
IEEE DOI 1412
Face; Feature extraction; Gold; Mouth; Standards; Training; Vectors BibRef

Jiang, B.[Bihan], Valstar, M.F.[Michel F.], Pantic, M.[Maja],
Action unit detection using sparse appearance descriptors in space-time video volumes,
FG11(314-321).
IEEE DOI 1103
BibRef

Mahoor, M.H.[Mohammad H.], Zhou, M.[Mu], Veon, K.L.[Kevin L.], Mavadati, S.M.[S. Mohammad], Cohn, J.F.[Jeffrey F.],
Facial action unit recognition with sparse representation,
FG11(336-342).
IEEE DOI 1103
BibRef

Simon, T.[Tomas], Nguyen, M.H.[Minh Hoai], de la Torre, F.[Fernando], Cohn, J.F.[Jeffrey F.],
Action unit detection with segment-based SVMs,
CVPR10(2737-2744).
IEEE DOI 1006
BibRef

Zor, C.[Cemre], Windeatt, T.[Terry],
Upper Facial Action Unit Recognition,
ICB09(239-248).
Springer DOI 0906
BibRef

Windeatt, T.[Terry], Dias, K.[Kaushala],
Ensemble Approaches to Facial Action Unit Classification,
CIARP08(551-559).
Springer DOI 0809
BibRef

Zhang, L.[Lei], Tong, Y.[Yan], Ji, Q.A.[Qi-Ang],
Active Image Labeling and Its Application to Facial Action Labeling,
ECCV08(II: 706-719).
Springer DOI 0810
BibRef
And:
Interactive labeling of facial action units,
ICPR08(1-4).
IEEE DOI 0812
BibRef

de Campos, C.P.[Cassio P.], Tong, Y.[Yan], Ji, Q.A.[Qi-Ang],
Constrained Maximum Likelihood Learning of Bayesian Networks for Facial Action Recognition,
ECCV08(III: 168-181).
Springer DOI 0810
BibRef

de Campos, C.P.[Cassio P.], Ji, Q.A.[Qi-Ang],
Improving Bayesian Network parameter learning using constraints,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Xu, S.A.[Shu-Ang], Jia, Y.D.[Yun-De], Zhang, X.X.[Xiao-Xun],
Coding Facial Expression with Oriented Steerable Filters,
ICIP06(2057-2060).
IEEE DOI 0610
BibRef

McCall, J.C.[Joel C.], Trivedi, M.M.[Mohan M.],
Pose invariant affect analysis using thin-plate splines,
ICPR04(III: 958-964).
IEEE DOI 0409
Facial landmark tracking, feature vector extraction and expression recognition. BibRef

McCall, J.C.[Joel C.], Trivedi, M.M.[Mohan M.],
Facial Action Coding Using Multiple Visual Cues and a Hierarchy of Particle Filters,
V4HCI06(150).
IEEE DOI 0609
BibRef

Whitehill, J., Omlin, C.W.,
Haar Features for FACS AU Recognition,
FGR06(97-101).
IEEE DOI 0604
BibRef

Whitehill, J., Omlin, C.W.,
Local versus Global Segmentation for Facial Expression Recognition,
FGR06(357-362).
IEEE DOI 0604
BibRef

Tong, Y.[Yan], Liao, W.H.[Wen-Hui], Ji, Q.A.[Qi-Ang],
Inferring Facial Action Units with Causal Relations,
CVPR06(II: 1623-1630).
IEEE DOI 0606
BibRef

Lucey, P.[Patrick], Lucey, S.[Simon], Cohn, J.F.[Jeffrey F.],
Registration Invariant Representations for Expression Detection,
DICTA10(255-261).
IEEE DOI 1012
BibRef

Wang, Y.[Yang], Lucey, S.[Simon], Cohn, J.F.[Jeffrey F.],
Non-Rigid Object Alignment with a Mismatch Template Based on Exhaustive Local Search,
NRTL07(1-8).
IEEE DOI 0710
BibRef

de la Torre, F.[Fernando], Campoy, J.[Joan], Ambadar, Z.[Zara], Cohn, J.F.[Jeffrey F.],
Temporal Segmentation of Facial Behavior,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Lucey, S., Matthews, I., Hu, C.B.[Chang-Bo], Ambadar, Z., de la Torre, F., Cohn, J.F.,
AAM Derived Face Representations for Robust Facial Action Recognition,
FGR06(155-162).
IEEE DOI
HTML Version. 0604

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Sifakis, E.[Eftychios], Fedkiw, R.[Ron],
Facial Muscle Activations from Motion Capture,
CVPR05(II: 1195).
IEEE DOI 0507
BibRef

Wang, H.C.[Hong-Cheng], Ahuja, N.,
Facial expression decomposition,
ICCV03(958-965).
IEEE DOI 0311
BibRef

Fasel, B.[Beat], Luettin, J.[Juergen],
Recognition of Asymmetric Facial Action Unit Activities and Intensities,
ICPR00(Vol I: 1100-1103).
IEEE DOI 0009
BibRef

Colmenarez, A.J.[Antonio J.], Frey, B.J.[Brendan J.], Huang, T.S.[Thomas S.],
A Probabilistic Framework for Embedded Face and Facial Expression Recognition,
CVPR99(I: 592-597).
IEEE DOI BibRef 9900
And:
Embedded Face and Facial Expression Recognition,
ICIP99(I:633-637).
IEEE DOI BibRef

Frey, B.J.[Brendan J.], Colmenarez, A.J., Huang, T.S.,
Mixtures of Local Linear Subspaces for Face Recognition,
CVPR98(32-37).
IEEE DOI BibRef 9800

Weng, J.[John], Hwang, W.S.[Wey-Shiuan],
Sensorimotor Action Sequence Learning with Application to Face Recognition Under Discourse,
ICPR98(Vol I: 252-254).
IEEE DOI 9808
BibRef

Saji, H.[Hitoshi], Nakatani, H.[Hiromasa], Ohta, H.[Hiroshi],
Recognition of Facial Expressions Using Muscle-Based Feature Models,
ICPR98(Vol II: 1379-1381).
IEEE DOI 9808
BibRef

Ishikawa, T.[Takahiro], Yazaki, K.[Kazuhiko], Sera, H.[Hajime], Morishima, S.[Shigeo], Terzopoulos, D.[Demetri],
Facial Muscle Parameter Decision from 2D Frontal Image,
ICPR98(Vol I: 160-162).
IEEE DOI 9808
BibRef

Eisert, P., and Girod, B.,
Model-Based Estimation of Facial Expression Parameters from Image Sequences,
ICIP97(II: 418-421).
IEEE DOI BibRef 9700

Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Spontaneous Extpressions .