21.2.3 Face Recognition Systems Using Neural Networks, Learning

Chapter Contents (Back)
Face Recognition. Learning. Application, Faces. Application, Face Recognition. Neural Networks. CNN.

Intrator, N., Reisfeld, D., Yeshurun, Y.,
Face Recognition Using A Hybrid Supervised Unsupervised Neural-Network,
PRL(17), No. 1, January 10 1996, pp. 67-76. BibRef 9601
Earlier:
Face Recognition Using a Hybrid Supervised/Unsupervised Neural Network,
ICPR94(B:50-54).
IEEE DOI BibRef

Edelman, S.[Shimon], Reisfeld, D.[Daniel], and Yeshurun, Y.[Yehezkel],
Learning to Recognize Faces from Examples,
ECCV92(787-791).
Springer DOI BibRef 9200
Earlier: WeizmannDepartment of Applied Mathematics and Computer Science, CS TR 91-20, 1991. BibRef

Reisfeld, D.[Daniel],
Generalized Symmetry Transforms: Attentional Mechanisms and Face Recognition,
Ph.D.Thesis, January 1994, BibRef 9401 Tel AvivUniversity. BibRef

Lando, M.[Maria], Edelman, S.[Shimon],
Generalization from a Single View in Face Recognition,
WeizmannCS-TR 95-02, 1995. BibRef 9500

Flocchini, P., Gardin, F., Mauri, G., Pensini, M.P., Stofella, P.,
Combining Image Processing Operators and Neural Networks in a Face Recognition System,
PRAI(6), 1992, pp. 447-467. BibRef 9200

Tamura, S., Kawai, H., Mitsumoto, H.,
Male-Female Identification from 8x6 Very-Low Resolution Face Images by Neural-Network,
PR(29), No. 2, February 1996, pp. 331-335.
Elsevier DOI BibRef 9602

Zhang, M.[Ming], Fucher, J.[John],
Face Recognition Using Artificial Neural-Network Group-Based Adaptive Tolerance (GAT) Trees,
TNN(7), No. 3, May 1996, pp. 555-567. 9606
BibRef

Zhang, M.[Ming], Fucher, J.[John],
Face perspective understanding using artificial neural network group-based tree,
ICIP96(III: 475-478).
IEEE DOI BibRef 9600

Javidi, B.[Bahram],
Method and apparatus for implementation of neural networks for face recognition,
US_Patent5,699,449, Dec 16, 1997
WWW Link. BibRef 9712

Lawrence, S.[Steve], Giles, C.L.[C. Lee], Tsoi, A.C.[Ah Chung], Back, A.D.[Andrew D.],
Face Recognition: A Convolutional Neural-Network Approach,
TNN(8), No. 1, January 1997, pp. 98-113. 9701
BibRef

Lawrence, S.[Steve], Giles, C.L.[C. Lee], Tsoi, A.C.[Ah Chung], Back, A.D.[Andrew D.],
Face Recognition: A Hybrid Neural Network Approach,
U. of Maryland-CS-TR-3608 and UMIACS-96-16. 1996.
WWW Link. BibRef 9600

Lawrence, S.[Steve], Giles, C.L.[C. Lee], Tsoi, A.C.[Ah Chung],
Convolutional Neural Networks for Face Recognition,
CVPR96(217-222).
IEEE DOI BibRef 9600

Uwechue, O.A.[Okechukwu A.], Pandya, A.S.[Abhijit S.],
Human Face Recognition Using Third-Order Synthetic Neural Networks,
KluwerJune 1997, ISBN 0-7923-9957-9.
WWW Link. BibRef 9706

Ranganath, S., Arun, K.,
Face Recognition Using Transform Features and Neural Networks,
PR(30), No. 10, October 1997, pp. 1615-1622.
Elsevier DOI 9712
BibRef

Yoon, K.S., Ham, Y.K., Park, R.H.,
Hybrid Approaches to Frontal View Face Recognition Using the Hidden Markov Model and Neural-Network,
PR(31), No. 3, March 1998, pp. 283-293.
Elsevier DOI 9802

See also 3D Object Recognition in Range Images Using Hidden Markov Models and Neural Networks. BibRef

Park, G.T.[Gyu-Tae], Bien, Z.N.[Zeung-Nam],
Neural network-based fuzzy observer with application to facial analysis,
PRL(21), No. 2, February 2000, pp. 93-105. 0003
BibRef

Dailey, M.N., and Cottrell, G.W.,
Organization of face and object recognition in modular neural network models,
NeurNet(12), Issues 7-8, 11 October 1999, pp. 1053-1074.
Elsevier DOI BibRef 9910

Hwang, W.S.[Wey-Shiuan], Weng, J.Y.[Ju-Yang],
Hierarchical Discriminant Regression,
PAMI(22), No. 11, November 2000, pp. 1277-1293.
IEEE DOI 0012
Classification system. Applied to faces. BibRef

Weng, J.J., Hwang, W.S.,
Toward Automation of Learning: The State Self-Organization Problem for a Face Recognizer,
AFGR98(384-389).
IEEE DOI BibRef 9800

Haddadnia, J.[Javad], Faez, K.[Karim], Ahmadi, M.[Majid],
A fuzzy hybrid learning algorithm for radial basis function neural network with application in human face recognition,
PR(36), No. 5, May 2003, pp. 1187-1202.
Elsevier DOI 0301
BibRef

Haddadnia, J.[Javad], Ahmadi, M.[Majid],
N-Feature Neural Network Human Face Recognition,
IVC(22), No. 12, 1 October 2004, pp. 1071-1082.
Elsevier DOI 0409
BibRef
Earlier: Add A2 of 3: Faez, K.[Karim], VI02(300).
PDF File. 0208
BibRef

Haddadnia, J., Faez, K., Moallem, P.,
Neural Network Based Face Recognition with Moment Invariants,
ICIP01(I: 1018-1021).
IEEE DOI 0108
BibRef

Yaghoubi, Z., Faez, K., Eliasi, M., Motamed, S.,
Face recognition using HMAX method for feature extraction and support vector machine classifier,
IVCNZ09(421-424).
IEEE DOI 0911
BibRef

Zhang, D., Peng, H.[Hui], Zhou, J.[Jie], Pal, S.K.,
A novel face recognition system using hybrid neural and dual eigenspaces methods,
SMC-A(32), No. 6, November 2002, pp. 787-793.
IEEE Top Reference. 0301
BibRef

Zhao, Z.Q.[Zhong-Qiu], Huang, D.S.[De-Shuang], Sun, B.Y.[Bing-Yu],
Human face recognition based on multi-features using neural networks committee,
PRL(25), No. 12, September 2004, pp. 1351-1358.
Elsevier DOI 0409
BibRef

Zhao, Z.Q.[Zhong-Qiu],
A novel modular neural network for imbalanced classification problems,
PRL(30), No. 9, 1 July 2009, pp. 783-788.
Elsevier DOI 0905
Modular neural networks; Imbalanced classification; Time consumption; Classification performance BibRef

Garcia, C., Delakis, M.,
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection,
PAMI(26), No. 11, November 2004, pp. 1408-1423.
IEEE Abstract. 0410
BibRef
Earlier:
A neural architecture for fast and robust face detection,
ICPR02(II: 44-47).
IEEE DOI 0211
Rotated 20deg, turned 60deg. Learn features extractors for recognition. BibRef

Ebrahimpour, R.[Reza], Kabir, E.[Ehsanollah], and Yousefi, M.R.[Mohammad Reza],
Teacher-directed learning in view-independent face recognition with mixture of experts using single-view eigenspaces,
Franklin(345), No. 2, March 2008, pp. 87-101.
Elsevier DOI BibRef 0803

Ebrahimpour, R.[Reza], Kabir, E.[Ehsanollah], Yousefi, M.R.[Mohammad Reza],
Teacher-directed learning in view-independent face recognition with mixture of experts using overlapping eigenspaces,
CVIU(111), No. 2, August 2008, pp. 195-206.
Elsevier DOI 0808
View-independent face recognition; Mixture of experts; Teacher-directed learning; Single-view eigenspaces; Global eigenspace; Overlapping eigenspaces BibRef

Ebrahimpour, R.[Reza], Kabir, E.[Ehsanollah], Yousefi, M.R.[Mohammad Reza],
Improving mixture of experts for view-independent face recognition using teacher-directed learning,
MVA(22), No. 2, March 2011, pp. 421-432.
WWW Link. 1103
BibRef

Ebrahimpour, R.[Reza], Kabir, E.[Ehsanollah], Esteky, H.[Hossein], and Yousefi, M.R.[Mohammad Reza],
View-independent face recognition with Mixture of Experts,
Neurocomputing(71), Issues 4-6, January 2008, pp. 1103-1107.
Elsevier DOI BibRef 0801

Kumar, D.[Dinesh], Rai, C.S., Kumar, S.[Shakti],
Analysis of unsupervised learning techniques for face recognition,
IJIST(20), No. 3, September 2010, pp. 261-267.
DOI Link 1008
BibRef

Sudha, N., Mohan, A.R., Meher, P.K.,
A Self-Configurable Systolic Architecture for Face Recognition System Based on Principal Component Neural Network,
CirSysVideo(21), No. 8, August 2011, pp. 1071-1084.
IEEE DOI 1108
BibRef

Chan, T.H.[Tsung-Han], Jia, K.[Kui], Gao, S.H.[Sheng-Hua], Lu, J.W.[Ji-Wen], Zeng, Z.[Zinan], Ma, Y.[Yi],
PCANet: A Simple Deep Learning Baseline for Image Classification?,
IP(24), No. 12, December 2015, pp. 5017-5032.
IEEE DOI 1512
channel bank filters BibRef

Zeng, Z.[Zinan], Xiao, S.J.[Shi-Jie], Jia, K.[Kui], Chan, T.H.[Tsung-Han], Gao, S.H.[Sheng-Hua], Xu, D.[Dong], Ma, Y.[Yi],
Learning by Associating Ambiguously Labeled Images,
CVPR13(708-715)
IEEE DOI 1309
low rank; partial permutation matrix; weakly supervised learning Faces with partial captions. BibRef

Xiong, C., Liu, L., Zhao, X., Yan, S., Kim, T.K.,
Convolutional Fusion Network for Face Verification in the Wild,
CirSysVideo(26), No. 3, March 2016, pp. 517-528.
IEEE DOI 1603
Accuracy BibRef

Xiong, C., Zhao, X., Tang, D., Jayashree, K., Yan, S., Kim, T.K.,
Conditional Convolutional Neural Network for Modality-Aware Face Recognition,
ICCV15(3667-3675)
IEEE DOI 1602
Convolution BibRef

Bondi, L., Baroffio, L., Cesana, M., Tagliasacchi, M., Chiachia, G., Rocha, A.,
Rate-energy-accuracy optimization of convolutional architectures for face recognition,
JVCIR(36), No. 1, 2016, pp. 142-148.
Elsevier DOI 1603
Convolutional architectures BibRef

Lv, J.J.[Jiang-Jing], Cheng, C.[Cheng], Tian, G.D.[Guo-Dong], Zhou, X.D.[Xiang-Dong], Zhou, X.[Xi],
Landmark perturbation-based data augmentation for unconstrained face recognition,
SP:IC(47), No. 1, 2016, pp. 465-475.
Elsevier DOI 1610
Feature representation BibRef

Deng, W.H.[Wei-Hong], Hu, J.[Jiani], Zhang, N.[Nanhai], Chen, B.H.[Bing-Hui], Guo, J.[Jun],
Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership,
PR(66), No. 1, 2017, pp. 63-73.
Elsevier DOI 1704
Fine-grained visual recognition. Includes a fine-grained dataset.
See also Lighting-aware face frontalization for unconstrained face recognition.
See also Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach. BibRef

Reale, C.[Christopher], Lee, H.[Hyungtae], Kwon, H.S.[Hee-Sung], Chellappa, R.[Rama],
Deep Network Shrinkage Applied to Cross-Spectrum Face Recognition,
FG17(897-903)
IEEE DOI 1707
Ad hoc networks, Convolution, Face, Face recognition, Machine learning, Optimization, Training BibRef

Reale, C.[Christopher], Lee, H.[Hyungtae], Kwon, H.S.[Hee-Sung],
Deep Heterogeneous Face Recognition Networks Based on Cross-Modal Distillation and an Equitable Distance Metric,
PBVS17(226-232)
IEEE DOI 1709
Cameras, Convolution, Face, Face recognition, Measurement, Neural networks, Training BibRef

Low, C.Y., Teoh, A.B.J., Toh, K.A.,
Stacking PCANet+: An Overly Simplified ConvNets Baseline for Face Recognition,
SPLetters(24), No. 11, November 2017, pp. 1581-1585.
IEEE DOI 1710
face recognition, image filtering, neural nets, principal component analysis, PCANet topology, BibRef

Ng, C.J.[Cong Jie], Low, C.Y.[Cheng Yaw], Toh, K.A.[Kar-Ann], Kim, J.H.[Jai-Hie], Teoh, A.B.J.[Andrew Beng Jin],
Orthogonal filter banks with region Log-Tied Rank covariance matrices for face recognition,
JVCIR(55), 2018, pp. 548-560.
Elsevier DOI 1809
Orthogonal filters, Region covariance matrices, Log-TiedRank, Face recognition BibRef

Hu, G., Peng, X., Yang, Y., Hospedales, T.M., Verbeek, J.[Jakob],
Frankenstein: Learning Deep Face Representations Using Small Data,
IP(27), No. 1, January 2018, pp. 293-303.
IEEE DOI 1712
data analysis, face recognition, image representation, learning (artificial intelligence), neural nets, small training data BibRef

Yin, X.[Xi], Liu, X.M.[Xiao-Ming],
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition,
IP(27), No. 2, February 2018, pp. 964-975.
IEEE DOI 1712
Estimation, Face, Face recognition, Feature extraction, Testing, Training, CNN, Multi-task learning, disentangled representation, pose-invariant face recognition BibRef

Tran, L.[Luan], Yin, X.[Xi], Liu, X.M.[Xiao-Ming],
Representation Learning by Rotating Your Faces,
PAMI(41), No. 12, December 2019, pp. 3007-3021.
IEEE DOI 1911
BibRef
Earlier:
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition,
CVPR17(1283-1292)
IEEE DOI 1711
Face recognition, Generators, Generative adversarial networks, Image generation, Image quality, Task analysis, face rotation and frontalization. Decoding, Face, Games, Generators, Image generation. BibRef

Grm, K.[Klemen], Štruc, V.[Vitomir], Artiges, A.[Anais], Caron, M.[Matthieu], Ekenel, H.K.[Hazim K.],
Strengths and weaknesses of deep learning models for face recognition against image degradations,
IET-Bio(7), No. 1, January 2018, pp. 81-89.
DOI Link 1712
BibRef

Al-Waisy, A.S.[Alaa S.], Qahwaji, R.[Rami], Ipson, S.[Stanley], Al-Fahdawi, S.[Shumoos],
A multimodal deep learning framework using local feature representations for face recognition,
MVA(29), No. 1, January 2018, pp. 35-54.
Springer DOI 1801
BibRef

Chen, G.H.[Guan-Hao], Shao, Y.Q.[Yan-Qing], Tang, C.W.[Chao-Wei], Jin, Z.[Zhuoyi], Zhang, J.K.[Jin-Kun],
Deep transformation learning for face recognition in the unconstrained scene,
MVA(29), No. 3, April 2018, pp. 513-523.
Springer DOI 1804
BibRef

Shi, X.S.[Xiao-Shuang], Guo, Z.H.[Zhen-Hua], Xing, F.[Fuyong], Cai, J.Z.[Jin-Zheng], Yang, L.[Lin],
Self-learning for face clustering,
PR(79), 2018, pp. 279-289.
Elsevier DOI 1804
Face clustering, Patch-based two-dimensional reconstruction, Self-paced learning BibRef

Huang, R., Jiang, X.,
Off-Feature Information Incorporated Metric Learning for Face Recognition,
SPLetters(25), No. 4, April 2018, pp. 541-545.
IEEE DOI 1804
face recognition, feature extraction, learning (artificial intelligence), distance metric learning, pose and expression estimation BibRef

Zhang, Y.H.[Yan-Hong], Shang, K.[Kun], Wang, J.[Jun], Li, N.[Nan], Zhang, M.M.Y.[Monica M.Y.],
Patch strategy for deep face recognition,
IET-IPR(12), No. 5, May 2018, pp. 819-825.
DOI Link 1804
BibRef

Zhang, M.M.Y.[Monica M.Y.], Shang, K.[Kun], Wu, H.M.[Hua-Ming],
Deep compact discriminative representation for unconstrained face recognition,
SP:IC(75), 2019, pp. 118-127.
Elsevier DOI 1906
Convolutional neural network, Compact discriminative loss, Advanced compact discriminative loss, Face recognition BibRef

Zhuang, N.[Ni], Yan, Y.[Yan], Chen, S.[Si], Wang, H.Z.[Han-Zi], Shen, C.H.[Chun-Hua],
Multi-label learning based deep transfer neural network for facial attribute classification,
PR(80), 2018, pp. 225-240.
Elsevier DOI 1805
Transfer learning, Facial attribute classification, Multi-label learning, Deep learning, Convolutional neural networks BibRef

Mao, L.B.[Long-Biao], Yan, Y.[Yan], Xue, J.H.[Jing-Hao], Wang, H.Z.[Han-Zi],
Deep Multi-Task Multi-Label CNN for Effective Facial Attribute Classification,
AffCom(13), No. 2, April 2022, pp. 818-828.
IEEE DOI 2206
Facial features, Task analysis, Feature extraction, Complexity theory, Network architecture, Face, Training, convolutional neural network BibRef

Zhuang, N., Yan, Y., Chen, S., Wang, H.Z.[Han-Zi],
Multi-task Learning of Cascaded CNN for Facial Attribute Classification,
ICPR18(2069-2074)
IEEE DOI 1812
Facial features, Task analysis, Feature extraction, Face, Training, Testing BibRef

Huo, J., Gao, Y., Shi, Y., Yang, W., Yin, H.,
Heterogeneous Face Recognition by Margin-Based Cross-Modality Metric Learning,
Cyber(48), No. 6, June 2018, pp. 1814-1826.
IEEE DOI 1805
Face, Face recognition, Feature extraction, Force, Measurement, Optimization, Training, Face recognition, large margin classifier, multimodality learning BibRef

Chen, C.H., Patel, V.M.[Vishal M.], Chellappa, R.[Rama],
Learning from Ambiguously Labeled Face Images,
PAMI(40), No. 7, July 2018, pp. 1653-1667.
IEEE DOI 1806
Face, Ice, Iterative methods, Labeling, Matrices, Matrix converters, Training data, Ambiguous learning, matrix completion BibRef

Zhao, M.J.[Meng-Jie], Song, B.[Bin], Zhang, Y.[Yue], Qin, H.[Hao],
Face verification based on deep Bayesian convolutional neural network in unconstrained environment,
SIViP(12), No. 5, July 2018, pp. 819-826.
Springer DOI 1806
BibRef

Wang, F., Cheng, J., Liu, W., Liu, H.,
Additive Margin Softmax for Face Verification,
SPLetters(25), No. 7, July 2018, pp. 926-930.
IEEE DOI 1807
face recognition, image classification, learning (artificial intelligence), minimisation, metric learning BibRef

Franc, V.[Vojtech], Cech, J.[Jan],
Learning CNNs from weakly annotated facial images,
IVC(77), 2018, pp. 10-20.
Elsevier DOI 1809
BibRef
Earlier:
Learning CNNs for Face Recognition from Weakly Annotated Images,
FG17(933-940)
IEEE DOI 1707
Convolution neural networks, EM algorithm, Face recognition, Age and gender prediction, Weak annotations Convolution, Databases, Detectors, Estimation, Face. BibRef

Wu, R.J.[Ren-Jie], Kamata, S.I.[Sei-Ichiro],
Sparse Graph Based Deep Learning Networks for Face Recognition,
IEICE(E101-D), No. 9, September 2018, pp. 2209-2219.
WWW Link. 1809
BibRef
Earlier:
A jointly local structured sparse deep learning network for face recognition,
ICIP16(3026-3030)
IEEE DOI 1610
Databases BibRef

Wu, R.J.[Ren-Jie], Kamata, S.I.[Sei-Ichiro],
Generic Sparse Graph Based Convolutional Networks for Face Recognition,
ICIP21(1589-1593)
IEEE DOI 2201
Face recognition, Image processing, Clustering methods, Benchmark testing, Convolutional neural networks, Sparse graph, Graph convolutional network BibRef

Ferrari, C., Lisanti, G., Berretti, S., del Bimbo, A.[Alberto],
Investigating Nuisances in DCNN-Based Face Recognition,
IP(27), No. 11, November 2018, pp. 5638-5651.
IEEE DOI 1809
BibRef
Earlier:
Investigating Nuisance Factors in Face Recognition with DCNN Representation,
Biometrics17(583-591)
IEEE DOI 1709
Face recognition, Face, Training, Machine learning, Computer architecture, Lighting, Standards, Face recognition, distance measures. Feature extraction, Training BibRef

McCurrie, M.[Mel], Beletti, F.[Fernando], Parzianello, L.[Lucas], Westendorp, A.[Allen], Anthony, S.E.[Samuel E.], Scheirer, W.J.[Walter J.],
Convolutional Neural Networks for Subjective Face Attributes,
IVC(78), 2018, pp. 14-25.
Elsevier DOI 1809
Psychophysics, Face attributes, Convolutional neural networks BibRef

Webster, B.R.[Brandon Richard], Kwon, S.Y.[So Yon], Clarizio, C.[Christopher], Anthony, S.E.[Samuel E.], Scheirer, W.J.[Walter J.],
Visual Psychophysics for Making Face Recognition Algorithms More Explainable,
ECCV18(XV: 263-281).
Springer DOI 1810
BibRef

Mygdalis, V.[Vasileios], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Exploiting multiplex data relationships in Support Vector Machines,
PR(85), 2019, pp. 70-77.
Elsevier DOI 1810
BibRef
Earlier:
Exploiting local and global geometric data relationships in Support Vector Data Description,
ICPR16(515-519)
IEEE DOI 1705
Multiplex data relationships, Support Vector Machine, Graph-based regularization, Multiple Kernel Learning. Data models, Face recognition, Kernel, Optimization, Training, data BibRef

Trigueros, D.S.[Daniel Sáez], Meng, L.[Li], Hartnett, M.[Margaret],
Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss,
IVC(79), 2018, pp. 99-108.
Elsevier DOI 1811
Face recognition, Convolutional neural networks, Facial occlusions, Distance metric learning BibRef

He, L., Li, H., Zhang, Q., Sun, Z.,
Dynamic Feature Matching for Partial Face Recognition,
IP(28), No. 2, February 2019, pp. 791-802.
IEEE DOI 1811
BibRef
Earlier:
Dynamic Feature Learning for Partial Face Recognition,
CVPR18(7054-7063)
IEEE DOI 1812
face recognition, feature extraction, image classification, image matching, image representation, neural nets, partial face recognition. Face, Probes, Convolution, Databases BibRef

Wu, Y.[Yue], Liu, H.F.[Hong-Fu], Li, J.[Jun], Fu, Y.[Yun],
Improving face representation learning with center invariant loss,
IVC(79), 2018, pp. 123-132.
Elsevier DOI 1811
Face recognition, Convolutional Neural Network, Center invariant loss BibRef

Oza, P., Patel, V.M.,
One-Class Convolutional Neural Network,
SPLetters(26), No. 2, February 2019, pp. 277-281.
IEEE DOI 1902
convolutional neural nets, entropy, face recognition, image classification, image representation, representation learning BibRef

Du, L., Hu, H.,
Face Recognition Using Simultaneous Discriminative Feature and Adaptive Weight Learning Based on Group Sparse Representation,
SPLetters(26), No. 3, March 2019, pp. 390-394.
IEEE DOI 1903
face recognition, feature extraction, image classification, image representation, learning (artificial intelligence), face recognition BibRef

Guo, A.J.X., Zhu, F.,
Spectral-Spatial Feature Extraction and Classification by ANN Supervised With Center Loss in Hyperspectral Imagery,
GeoRS(57), No. 3, March 2019, pp. 1755-1767.
IEEE DOI 1903
face recognition, feature extraction, hyperspectral imaging, image classification, learning (artificial intelligence), hyperspectral image classification BibRef

Cui, Z.[Zhen], Xiao, S.T.[Sheng-Tao], Niu, Z.H.[Zhi-Heng], Yan, S.C.[Shui-Cheng], Zheng, W.M.[Wen-Ming],
Recurrent Shape Regression,
PAMI(41), No. 5, May 2019, pp. 1271-1278.
IEEE DOI 1904
Shape, Feature extraction, Face, Training, Task analysis, Recurrent neural networks, Tools, Shape regression, face alignment BibRef

Deng, Z.Y.[Zhong-Ying], Peng, X.J.[Xiao-Jiang], Li, Z.F.[Zhi-Feng], Qiao, Y.[Yu],
Mutual Component Convolutional Neural Networks for Heterogeneous Face Recognition,
IP(28), No. 6, June 2019, pp. 3102-3114.
IEEE DOI 1905
Feature extraction, Face recognition, Analytical models, Convolutional neural networks, Face, Task analysis, mutual component convolutional neural network BibRef

Wen, Y.D.[Yan-Dong], Zhang, K.P.[Kai-Peng], Li, Z.F.[Zhi-Feng], Qiao, Y.[Yu],
A Discriminative Feature Learning Approach for Deep Face Recognition,
ECCV16(VII: 499-515).
Springer DOI 1611
BibRef
Earlier: A1, A3, A4, Only:
Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,
CVPR16(4893-4901)
IEEE DOI 1612
BibRef

Zhang, X.[Xiao], Fang, Z.Y.[Zhi-Yuan], Wen, Y.D.[Yan-Dong], Li, Z.F.[Zhi-Feng], Qiao, Y.[Yu],
Range Loss for Deep Face Recognition with Long-Tailed Training Data,
ICCV17(5419-5428)
IEEE DOI 1802
convolution, face recognition, neural nets, Labeled Faces in the Wild, convolutional neural networks, Training data BibRef

Wen, Y.D.[Yan-Dong], Zhang, K.P.[Kai-Peng], Li, Z.F.[Zhi-Feng], Qiao, Y.[Yu],
A Comprehensive Study on Center Loss for Deep Face Recognition,
IJCV(127), No. 6-7, June 2019, pp. 668-683.
Springer DOI 1906
BibRef

Hu, W.P.[Wei-Peng], Hu, H.F.[Hai-Feng],
Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition,
CVIU(184), 2019, pp. 9-21.
Elsevier DOI 1906
Heterogeneous face recognition, Deep learning, Joint supervision loss, Feature fusion BibRef

Hu, W.P.[Wei-Peng], Yan, W.J.[Wen-Jun], Hu, H.F.[Hai-Feng],
Dual Face Alignment Learning Network for NIR-VIS Face Recognition,
CirSysVideo(32), No. 4, April 2022, pp. 2411-2424.
IEEE DOI 2204
Face recognition, Image reconstruction, Feature extraction, Faces, Lighting, Task analysis, Hidden Markov models, cross-domain compact representation
See also Age Factor Removal Network Based on Transfer Learning and Adversarial Learning for Cross-Age Face Recognition. BibRef

Luo, X.L.[Xiao-Ling], Xu, Y.[Yong], Yang, J.[Jian],
Multi-resolution dictionary learning for face recognition,
PR(93), 2019, pp. 283-292.
Elsevier DOI 1906
Dictionary learning, Multi-resolution, Face recognition BibRef

Zhao, J.[Jian], Xiong, L.[Lin], Li, J.S.[Jian-Shu], Xing, J.L.[Jun-Liang], Yan, S.C.[Shui-Cheng], Feng, J.S.[Jia-Shi],
3D-Aided Dual-Agent GANs for Unconstrained Face Recognition,
PAMI(41), No. 10, October 2019, pp. 2380-2394.
IEEE DOI 1909
Face, Face recognition, Training, Generators, Solid modeling, generative adversarial networks BibRef

Zhao, J.[Jian], Xing, J.L.[Jun-Liang], Xiong, L.[Lin], Yan, S.C.[Shui-Cheng], Feng, J.S.[Jia-Shi],
Recognizing Profile Faces by Imagining Frontal View,
IJCV(128), No. 2, February 2020, pp. 460-478.
Springer DOI 2002
BibRef

Wei, X.[Xin], Wang, H.[Hui], Scotney, B.W.[Bryan W.], Wan, H.[Huan],
Minimum Margin Loss for Deep Face Recognition,
PR(97), 2020, pp. 107012.
Elsevier DOI 1910
BibRef
And:
Precise Adjacent Margin Loss for Deep Face Recognition,
ICIP19(3641-3645)
IEEE DOI 1910
Deep learning, Convolutional neural networks, Face recognition, Minimum margin loss. Margin, Loss, Deep learning. BibRef

Zhang, L., Liu, J., Zhang, B., Zhang, D., Zhu, C.,
Deep Cascade Model-Based Face Recognition: When Deep-Layered Learning Meets Small Data,
IP(29), No. , 2020, pp. 1016-1029.
IEEE DOI 1911
Image coding, Encoding, Face recognition, Nuclear magnetic resonance, Deep learning, Data models, corruption BibRef

Ding, Z., Shao, M., Hwang, W., Suh, S., Han, J., Choi, C., Fu, Y.,
Robust Discriminative Metric Learning for Image Representation,
CirSysVideo(29), No. 11, November 2019, pp. 3173-3183.
IEEE DOI 1911
Measurement, Data models, Noise reduction, Optimization, Face recognition, Feature extraction, fast low-rank representation BibRef

Wang, Q.C.[Qiang-Chang], Guo, G.D.[Guo-Dong],
Benchmarking deep learning techniques for face recognition,
JVCIR(65), 2019, pp. 102663.
Elsevier DOI 1912
Deep learning, Convolutional neural networks, Face recognition, GPU, PyTorch, TensorFlow, Caffe, AlexNet, ArcFace, Center-loss, VGG BibRef

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A deep learning approach for face recognition based on angularly discriminative features,
PRL(128), 2019, pp. 414-419.
Elsevier DOI 1912
Face recognition, Loss function, Angular margin, Additive margin, Face dataset BibRef

Zou, F.H.[Fu-Hao], Yang, F.[Fan], Chen, W.[Wei], Li, K.[Kai], Song, J.K.[Jing-Kuan], Chen, J.C.[Jing-Cai], Ling, H.[Hefei],
Fast large scale deep face search,
PRL(130), 2020, pp. 83-90.
Elsevier DOI 2002
Face recognition, Semantic hashing, Deep convolution neural network, Face search BibRef

Iranmanesh, S.M.[Seyed Mehdi], Riggan, B.[Benjamin], Hu, S.[Shuowen], Nasrabadi, N.M.[Nasser M.],
Coupled generative adversarial network for heterogeneous face recognition,
IVC(94), 2020, pp. 103861.
Elsevier DOI 2003
Heterogeneous face recognition, Generative adversarial networks, Face verification, Biometrics BibRef

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A high-efficiency energy and storage approach for IoT applications of facial recognition,
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Elsevier DOI 2005
Data compression, Face recognition, IoT, Deep learning BibRef

Liu, L.[Li], Chen, S.Q.[Si-Qi], Chen, X.X.[Xiu-Xiu], Wang, T.S.[Tian-Shi], Zhang, L.[Long],
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CVIU(197-198), 2020, pp. 102983.
Elsevier DOI 2008
Incremental Learning, Cumulative Learning, Memory Based Learning, Multiple Object Tracking, Long Term Object Tracking BibRef

Pernici, F.[Federico], Bartoli, F.[Federico], Bruni, M.[Matteo], del Bimbo, A.[Alberto],
Memory Based Online Learning of Deep Representations from Video Streams,
CVPR18(2324-2334)
IEEE DOI 1812
Face, Streaming media, Memory modules, Object tracking, Graphics processing units, Visualization, Learning systems BibRef

Liu, Y.F.[Yan-Fei], Chen, J.H.[Jun-Hua], Qiu, Y.[Yu],
Joint Multi-Patch and Multi-Task CNNs for Robust Face Recognition,
IEICE(E103-D), No. 10, October 2020, pp. 2178-2187.
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Coupled adversarial learning for semi-supervised heterogeneous face recognition,
PR(110), 2021, pp. 107618.
Elsevier DOI 2011
Adversarial learning, Heterogeneous face recognition, Deep representation BibRef

Zhu, Y.H.[Ying-Hui], Jiang, Y.Z.[Yu-Zhen],
Optimization of face recognition algorithm based on deep learning multi feature fusion driven by big data,
IVC(104), 2020, pp. 104023.
Elsevier DOI 2012
Big data, Face recognition, Deep learning, Multi-feature fusion BibRef

Massoli, F.V.[Fabio Valerio], Falchi, F.[Fabrizio], Amato, G.[Giuseppe],
Cross-resolution face recognition adversarial attacks,
PRL(140), 2020, pp. 222-229.
Elsevier DOI 2012
Deep learning, Face recognition, Adversarial attacks, Face identification, Adversarial biometrics BibRef

Low, C.Y., Park, J., Teoh, A. .B.J.[A. Beng-Jin],
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification,
Cyber(50), No. 12, December 2020, pp. 5021-5034.
IEEE DOI 2012
BibRef
Earlier: A1, A3, Only:
Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features,
ICIP17(1592-1596)
IEEE DOI 1803
Kernel, Training, Neural networks, Graphics processing units, Principal component analysis, Convolution, Cybernetics, stacking-based deep neural network (S-DNN). Deep analytic network, face recognition, multi-fold filter convolution, object recognition, spectral histogram BibRef

Wang, S.F.[Shang-Fei], Yin, S.[Shi], Hao, L.F.[Long-Fei], Liang, G.[Guang],
Multi-task face analyses through adversarial learning,
PR(114), 2021, pp. 107837.
Elsevier DOI 2103
Multi-task learning, Adversarial learning, Face analyses BibRef

Yang, S.M.[Shan-Ming], Deng, W.H.[Wei-Hong], Wang, M.[Mei], Du, J.P.[Jun-Ping], Hu, J.[Jiani],
Orthogonality Loss: Learning Discriminative Representations for Face Recognition,
CirSysVideo(31), No. 6, June 2021, pp. 2301-2314.
IEEE DOI 2106
Face recognition, Face, Feature extraction, Training, Robustness, Matrix decomposition, Benchmark testing, Face recognition, inter-class distance BibRef

Hu, W.[Wei], Huang, Y.Y.[Yang-Yu], Zhang, F.[Fan], Li, R.R.[Rui-Rui], Li, H.C.[Heng-Chao],
SeqFace: Learning discriminative features by using face sequences,
IET-IPR(15), No. 11, 2021, pp. 2548-2558.
DOI Link 2108

WWW Link. Code, Face Recognition. Training on face sequences from video. CNNs, face recognition, face sequences, training data augmentation BibRef

Biswas, R.[Rubel], González-Castro, V.[Víctor], Fidalgo, E.[Eduardo], Alegre, E.[Enrique],
A new perceptual hashing method for verification and identity classification of occluded faces,
IVC(113), 2021, pp. 104245.
Elsevier DOI 2108
E.g. occluded eye region. Face verification, Face classification, Adversarial attack eye region occlusion, Perceptual hashing, OSF-DNS BibRef

Wang, Q.C.[Qiang-Chang], Guo, G.D.[Guo-Dong],
AAN-Face: Attention Augmented Networks for Face Recognition,
IP(30), 2021, pp. 7636-7648.
IEEE DOI 2109
Face recognition, Feature extraction, Cams, Training data, Task analysis, Noise measurement, Faces, Masked face recognition, heterogeneous face recognition BibRef

Shao, H.C.[Hao-Chiang], Liu, K.Y.[Kang-Yu], Su, W.T.[Weng-Tai], Lin, C.W.[Chia-Wen], Lu, J.W.[Ji-Wen],
DotFAN: A Domain-Transferred Face Augmentation Net,
IP(30), 2021, pp. 8759-8772.
IEEE DOI 2111
BibRef
Earlier: A1, A2, A4, A5, Only:
Domain-Transferred Face Augmentation Network,
ACCV20(VI:309-325).
Springer DOI 2103
Face recognition, Training, Lighting, Finite element analysis, Codes, Data models, Face augmentation, generative model BibRef

Wang, J.Q.[Jia-Qi], Zheng, C.[Chen], Yang, X.H.[Xiao-Hui], Yang, L.J.[Li-Jun],
EnhanceFace: Adaptive Weighted SoftMax Loss for Deep Face Recognition,
SPLetters(29), 2022, pp. 65-69.
IEEE DOI 2202
Training, Face recognition, Loss measurement, Weight measurement, Signal processing algorithms, Optimization, Feature extraction, adaptive weight BibRef

Zhong, Y.Y.[Yao-Yao], Deng, W.H.[Wei-Hong], Fang, H.[Han], Hu, J.[Jiani], Zhao, D.[Dongyue], Li, X.[Xian], Wen, D.[Dongchao],
Dynamic Training Data Dropout for Robust Deep Face Recognition,
MultMed(24), 2022, pp. 1186-1197.
IEEE DOI 2203
Training, Face recognition, Databases, Predictive models, Noise measurement, Training data, Data models, Face recognition, training BibRef

Liang, Z.X.[Ze-Xiao], Zeng, D.Y.[De-Yu], Guo, S.Z.[Shao-Zhi], Li, J.Z.[Jian-Zhong], Wu, Z.Z.[Zong-Ze],
A fusion representation for face learning by low-rank constrain and high-frequency texture components,
PRL(155), 2022, pp. 48-53.
Elsevier DOI 2203
High-frequency signal, Texture component, Low-Rank representation BibRef

Wang, X.B.[Xiao-Bo], Wang, S.[Shuo], Liang, Y.Y.[Yan-Yan], Gu, L.[Liang], Lei, Z.[Zhen],
RVFace: Reliable Vector Guided Softmax Loss for Face Recognition,
IP(31), 2022, pp. 2337-2351.
IEEE DOI 2203
Face recognition, Noise measurement, Training, Reliability, Representation learning, Additives, Feature extraction, discriminative feature learning BibRef

Holkar, A.[Ashwamegha], Walambe, R.[Rahee], Kotecha, K.[Ketan],
Few-Shot learning for face recognition in the presence of image discrepancies for limited multi-class datasets,
IVC(120), 2022, pp. 104420.
Elsevier DOI 2204
Few-Shot learning, Face recognition, Occlusion, Low light, Orientation, Siamese networks BibRef

Shi, X.[Xiao], Chai, X.J.[Xiu-Juan], Xie, J.[Jiake], Sun, T.[Tan],
MC-GCN: A Multi-Scale Contrastive Graph Convolutional Network for Unconstrained Face Recognition With Image Sets,
IP(31), 2022, pp. 3046-3055.
IEEE DOI 2205
Prototypes, Face recognition, Feature extraction, Semantics, Faces, Task analysis, Media, Face recognition, image set, multi-scale BibRef

Tsai, T.H.[Tsung-Han], Chi, P.T.[Po-Ting],
A single-stage face detection and face recognition deep neural network based on feature pyramid and triplet loss,
IET-IPR(16), No. 8, 2022, pp. 2148-2156.
DOI Link 2205
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Liu, Z.Z.[Zhi-Zhe], Zhang, X.X.[Xing-Xing], Zhu, Z.F.[Zhen-Feng], Zheng, S.[Shuai], Zhao, Y.[Yao], Cheng, J.[Jian],
MFHI: Taking Modality-Free Human Identification as Zero-Shot Learning,
CirSysVideo(32), No. 8, August 2022, pp. 5225-5237.
IEEE DOI 2208
Task analysis, Faces, Visualization, Face recognition, Training, Semantics, Prototypes, Human identification, zero-shot learning, deep learning BibRef

Lv, X.W.[Xian-Wei], Yu, C.[Chen], Jin, H.[Hai], Liu, K.[Kai],
HQ2CL: A High-Quality Class Center Learning System for Deep Face Recognition,
IP(31), 2022, pp. 5359-5370.
IEEE DOI 2208
Face recognition, Training, Feature extraction, Learning systems, Indexes, Deep learning, Training data, Face recognition, high-quality sample BibRef

Deng, J.K.[Jian-Kang], Guo, J.[Jia], Yang, J.[Jing], Xue, N.N.[Nian-Nan], Kotsia, I.[Irene], Zafeiriou, S.P.[Stefanos P.],
ArcFace: Additive Angular Margin Loss for Deep Face Recognition,
PAMI(44), No. 10, October 2022, pp. 5962-5979.
IEEE DOI 2209
BibRef
Earlier: A1, A2, A4, A6, Only: CVPR19(4685-4694).
IEEE DOI 2002
Face recognition, Training, Noise measurement, Training data, Additives, Predictive models, Data models, model inversion BibRef

Xiao, D.[Degui], Li, J.Z.[Jia-Zhi], Li, J.F.[Jian-Fang], Dong, S.P.[Shi-Ping], Lu, T.[Tao],
IHEM Loss: Intra-Class Hard Example Mining Loss for Robust Face Recognition,
CirSysVideo(32), No. 11, November 2022, pp. 7821-7831.
IEEE DOI 2211
Face recognition, Training, Measurement, Computational modeling, Representation learning, Convolutional neural networks, hard example mining BibRef

Huang, B.[Baojin], Wang, Z.Y.[Zhong-Yuan], Wang, G.C.[Guang-Cheng], Jiang, K.[Kui], Han, Z.[Zhen], Lu, T.[Tao], Liang, C.[Chao],
PLFace: Progressive Learning for Face Recognition with Mask Bias,
PR(135), 2023, pp. 109142.
Elsevier DOI 2212
Face recognition, Progressive learning, Mask bias BibRef

Jatain, R.[Rashmi], Jailia, M.[Manisha],
Enhanced Face Recognition Using Adaptive Local Tri Weber Pattern with Improved Deep Learning Architecture,
IJIG(22), No. 5 2022, pp. 2250052.
DOI Link 2212
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Su, W.C.[Wei-Cong], Wang, Y.[Yali], Li, K.[Kunchang], Gao, P.[Peng], Qiao, Y.[Yu],
Hybrid token transformer for deep face recognition,
PR(139), 2023, pp. 109443.
Elsevier DOI 2304
Face recognition, Hybrid tokens, Relation learning BibRef

Yang, J.[Jifan], Wang, Z.Y.[Zhong-Yuan], Huang, B.[Baojin], Xiao, J.S.[Jin-Sheng], Liang, C.[Chao], Han, Z.[Zhen], Zou, H.[Hua],
HeadPose-Softmax: Head pose adaptive curriculum learning loss for deep face recognition,
PR(140), 2023, pp. 109552.
Elsevier DOI 2305
Face recognition, Multi-view face, Curriculum learning, Pose-aware BibRef

Pankaj, Bharti, P.K., Kumar, B.[Brajesh],
A New Design of Occlusion-Invariant Face Recognition Using Optimal Pattern Extraction and CNN with GRU-Based Architecture,
IJIG(23), No. 4 2023, pp. 2350029.
DOI Link 2308
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Zhao, C.[Chaoyu], Qian, J.J.[Jian-Jun], Zhu, S.[Shumin], Xie, J.[Jin], Yang, J.[Jian],
Emphasizing Closeness and Diversity Simultaneously for Deep Face Representation,
ACCV22(IV:88-104).
Springer DOI 2307
BibRef

Banerjee, S.[Sandipan], Scheirer, W.J.[Walter J.], Bowyer, K.W.[Kevin W.], Flynn, P.J.[Patrick J.],
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks,
FG23(1-8)
IEEE DOI 2303
Training, Solid modeling, Shape, Face recognition, Perturbation methods, Training data BibRef

Lin, H.[Huawei], Liu, H.Z.[Hao-Zhe], Li, Q.[Qiufu], Shen, L.L.[Lin-Lin],
Activation Template Matching Loss for Explainable Face Recognition,
FG23(1-8)
IEEE DOI 2303
Measurement, Visualization, Annotations, Convolution, Face recognition, Nose, Mouth BibRef

Zhang, Y.[Yang], Herdade, S.[Simao], Thadani, K.[Kapil], Dodds, E.[Eric], Culpepper, J.[Jack], Ku, Y.N.[Yueh-Ning],
Unifying Margin-Based Softmax Losses in Face Recognition,
WACV23(3537-3546)
IEEE DOI 2302
Training, Manifolds, Sensitivity, Face recognition, Prototypes, Benchmark testing, Reliability theory, Algorithms: Biometrics BibRef

Zhao, Q.C.[Qing-Chao], Li, L.[Long], Chu, Y.[Yan], Wang, Z.K.[Zheng-Kui], Shan, W.[Wen],
Density Division Face Clustering Based on Graph Convolutional Networks,
ICPR22(5017-5023)
IEEE DOI 2212
Couplings, Face recognition, Clustering methods, Clustering algorithms, Prediction algorithms, Inference algorithms BibRef

Zhao, X.Y.[Xing-Ying], Jiang, H.[Hao], Shen, D.[Dong],
Eogface: Deep Face Recognition via Extensional Logits,
ICIP22(311-315)
IEEE DOI 2211
Image recognition, Codes, Face recognition, Memory management, Graphics processing units, Benchmark testing, Task analysis, Compactness BibRef

Wang, K.[Kai], Wang, S.[Shuo], Zhang, P.P.[Pan-Pan], Zhou, Z.P.[Zhi-Peng], Zhu, Z.[Zheng], Wang, X.B.[Xiao-Bo], Peng, X.J.[Xiao-Jiang], Sun, B.[Baigui], Li, H.[Hao], You, Y.[Yang],
An Efficient Training Approach for Very Large Scale Face Recognition,
CVPR22(4073-4082)
IEEE DOI 2210
Training, Deep learning, Technological innovation, Ethics, Costs, Face recognition, Face and gestures BibRef

Phan, H.[Hai], Nguyen, A.[Anh],
DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification,
CVPR22(20227-20237)
IEEE DOI 2210
Earth, Training, Law enforcement, Face recognition, Perturbation methods, Feature extraction, Biometrics, Explainable computer vision BibRef

An, X.[Xiang], Deng, J.K.[Jian-Kang], Guo, J.[Jia], Feng, Z.Y.[Zi-Yong], Zhu, X.H.[Xu-Han], Yang, J.[Jing], Liu, T.L.[Tong-Liang],
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC,
CVPR22(4032-4041)
IEEE DOI 2210
Training, Costs, Codes, Face recognition, Training data, Tail, Face and gestures, Biometrics BibRef

Liu, C.[Chang], Yu, X.[Xiang], Tsai, Y.H.[Yi-Hsuan], Faraki, M.[Masoud], Moslemi, R.[Ramin], Chandraker, M.[Manmohan], Fu, Y.[Yun],
Learning to Learn across Diverse Data Biases in Deep Face Recognition,
CVPR22(4062-4072)
IEEE DOI 2210
Training, Additives, Face recognition, Computational modeling, Predictive models, Benchmark testing, Face and gestures, Transfer/low-shot/long-tail learning BibRef

Boutros, F.[Fadi], Damer, N.[Naser], Kirchbuchner, F.[Florian], Kuijper, A.[Arjan],
ElasticFace: Elastic Margin Loss for Deep Face Recognition,
Biometrics22(1577-1586)
IEEE DOI 2210
Training, Codes, Face recognition, Buildings, Training data BibRef

Huang, Y.[Yuge], Wu, J.X.[Jia-Xiang], Xu, X.[Xingkun], Ding, S.H.[Shou-Hong],
Evaluation-oriented Knowledge Distillation for Deep Face Recognition,
CVPR22(18719-18728)
IEEE DOI 2210
Training, Performance evaluation, Knowledge engineering, Protocols, Face recognition, Benchmark testing, Face and gestures BibRef

El Alami, A.[Abdelmajid], Mesbah, A.[Abderrahim], Berrahou, N.[Nadia], Berrahou, A.[Aissam], Qjidaa, H.[Hassan],
Quaternion Discrete Racah Moments Convolutional Neural Network for Color Face Recognition,
ISCV22(1-5)
IEEE DOI 2208
Training, Image color analysis, Quaternions, Face recognition, Computer architecture, Color, Watermarking, complexity BibRef

GOOYA, E.S.[Ehsan SEDGH], FALOU, A.A.[Ayman AL], KADDAH, W.[Wissam],
Robust and discriminating face recognition system based on a neural network and correlation techniques,
IPTA20(1-5)
IEEE DOI 2206
Measurement, Correlation, Thresholding (Imaging), Face recognition, Neural networks, Stacking, Tools, neural networks, auto-encoder, face recognition BibRef

Zhang, Y.B.[Yao-Bin], Deng, W.H.[Wei-Hong], Zhong, Y.Y.[Yao-Yao], Hu, J.[Jiani], Li, X.[Xian], Zhao, D.[Dongyue], Wen, D.C.[Dong-Chao],
Adaptive Label Noise Cleaning with Meta-Supervision for Deep Face Recognition,
ICCV21(15045-15055)
IEEE DOI 2203
Training, Face recognition, Computational modeling, Transfer learning, Training data, Interference, Biometrics, Faces BibRef

Lin, C.H.[Chun-Hsien], Wu, B.F.[Bing-Fei],
Domain Adapting Ability of Self-Supervised Learning for Face Recognition,
ICIP21(479-483)
IEEE DOI 2201
Adaptation models, Protocols, Image recognition, Target recognition, Face recognition, Training data, Data models, self-supervised learning BibRef

Sepas-Moghaddam, A.[Alireza], Pereira, F.[Fernando], Correia, P.L.[Paulo Lobato], Etemad, A.[Ali],
Multi-Perspective LSTM for Joint Visual Representation Learning,
CVPR21(16535-16543)
IEEE DOI 2111
Visualization, Microprocessors, Face recognition, Lips, Computer architecture, Logic gates BibRef

Li, S.[Shen], Xu, J.Q.[Jian-Qing], Xu, X.Q.[Xia-Qing], Shen, P.C.[Peng-Cheng], Li, S.X.[Shao-Xin], Hooi, B.[Bryan],
Spherical Confidence Learning for Face Recognition,
CVPR21(15624-15632)
IEEE DOI 2111
Geometry, Face recognition, Benchmark testing, Probabilistic logic, Noise measurement, Task analysis BibRef

Deng, J.K.[Jian-Kang], Guo, J.[Jia], Yang, J.[Jing], Lattas, A.[Alexandros], Zafeiriou, S.P.[Stefanos P.],
Variational Prototype Learning for Deep Face Recognition,
CVPR21(11901-11910)
IEEE DOI 2111
Training, Learning systems, Face recognition, Memory management, Prototypes, Benchmark testing BibRef

Shen, S.[Shuai], Li, W.[Wanhua], Zhu, Z.[Zheng], Huang, G.[Guan], Du, D.L.[Da-Long], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Structure-Aware Face Clustering on a Large-Scale Graph with 107 Nodes,
CVPR21(9081-9090)
IEEE DOI 2111
Training, Face recognition, Design methodology, Memory management, Training data, Graphics processing units BibRef

Marriott, R.T.[Richard T.], Romdhani, S.[Sami], Chen, L.M.[Li-Ming],
A 3D GAN for Improved Large-pose Facial Recognition,
CVPR21(13440-13450)
IEEE DOI 2111
Training, Solid modeling, Face recognition, Lighting, Generative adversarial networks, Robustness BibRef

Liu, R.S.[Ru-Shuai], Tan, W.J.[Wei-Jun],
EQFace: A Simple Explicit Quality Network for Face Recognition,
AMFG21(1482-1490)
IEEE DOI 2109
Training, Knowledge engineering, Deep learning, Image recognition, Face recognition, Training data, Lighting BibRef

Arachchilage, S.W.[Samadhi Wickrama], Izquierdo, E.[Ebroul],
SSDL: Self-Supervised Domain Learning for Improved Face Recognition,
ICPR21(8117-8124)
IEEE DOI 2105
Adaptation models, Face recognition, Lighting, Benchmark testing, Sensors, Reliability BibRef

Wang, G.A.[Gao-Ang], Chen, L.[Lin], Liu, T.Q.[Tian-Qiang], He, M.W.[Ming-Wei], Luo, J.B.[Jie-Bo],
DAIL: Dataset-Aware and Invariant Learning for Face Recognition*,
ICPR21(8172-8179)
IEEE DOI 2105
Training, Image resolution, Face recognition, Cleaning, face recognition, dataset-aware, dataset-invariant, data cleaning, domain adaptation BibRef

Andriyanov, N.[Nikita], Dementev, V.[Vitaly], Tashlinskiy, A.[Alexandr], Vasiliev, K.[Konstantin],
The Study of Improving the Accuracy of Convolutional Neural Networks in Face Recognition Tasks,
IMTA20(5-14).
Springer DOI 2103
BibRef

Yin, X.[Xi], Tai, Y.[Ying], Huang, Y.[Yuge], Liu, X.M.[Xiao-Ming],
Fan: Feature Adaptation Network for Surveillance Face Recognition and Normalization,
ACCV20(II:301-319).
Springer DOI 2103
BibRef

Kim, I.[Insoo], Han, S.J.[Seung-Ju], Baek, J.W.[Ji-Won], Park, S.J.[Seong-Jin], Han, J.J.[Jae-Joon], Shin, J.[Jinwoo],
Quality-Agnostic Image Recognition via Invertible Decoder,
CVPR21(12252-12261)
IEEE DOI 2111
Training, Image recognition, Image resolution, Face recognition, Data models, Robustness, Decoding BibRef

Kim, I.[Insoo], Han, S.J.[Seung-Ju], Park, S.J.[Seong-Jin], Baek, J.W.[Ji-Won], Shin, J.[Jinwoo], Han, J.J.[Jae-Joon], Choi, C.K.[Chang-Kyu],
Discface: Minimum Discrepancy Learning for Deep Face Recognition,
ACCV20(V:358-374).
Springer DOI 2103
BibRef

Dhar, P., Bansal, A., Castillo, C.D., Gleason, J., Phillips, P.J., Chellappa, R.,
How are attributes expressed in face DCNNs?,
FG20(85-92)
IEEE DOI 2102
Face recognition, Training, Facial features, Sensitivity, Mutual information, Neural networks, Feature extraction BibRef

Zhao, H.[He], Shi, Y.J.[Yong-Jie], Tong, X.[Xin], Ying, X.H.[Xiang-Hua], Zha, H.B.[Hong-Bin],
Qamface: Quadratic Additive Angular Margin Loss For Face Recognition,
ICIP20(1901-1905)
IEEE DOI 2011
Face Recognition, Loss Function, Margin, ArcFace BibRef

Feng, Y.S.[Yu-Shu], Wang, H.[Huan], Hu, H.J.R.[Hao-Ji Roland], Yu, L.[Lu], Wang, W.[Wei], Wang, S.Y.[Shi-Yan],
Triplet Distillation For Deep Face Recognition,
ICIP20(808-812)
IEEE DOI 2011
Face Recognition, Knowledge Distillation, Triplet Loss, Network Compression BibRef

Kim, Y., Park, W., Roh, M., Shin, J.,
GroupFace: Learning Latent Groups and Constructing Group-Based Representations for Face Recognition,
CVPR20(5620-5629)
IEEE DOI 2008
Face recognition, Face, Machine learning, Training, Measurement, Labeling, Computational modeling BibRef

Yu, H., Zheng, W.,
Weakly Supervised Discriminative Feature Learning With State Information for Person Identification,
CVPR20(5527-5537)
IEEE DOI 2008
Cameras, Task analysis, Visualization, Distortion, Face recognition, Scalability, Training BibRef

Chang, J., Lan, Z., Cheng, C., Wei, Y.,
Data Uncertainty Learning in Face Recognition,
CVPR20(5709-5718)
IEEE DOI 2008
Uncertainty, Face, Face recognition, Noise measurement, Data models, Training, Gaussian distribution BibRef

Zhang, Y., Deng, W.,
Class-Balanced Training for Deep Face Recognition,
Biometrics20(3594-3603)
IEEE DOI 2008
Training, Face recognition, Face, Benchmark testing, Data structures, Data models BibRef

Wang, Q., Wu, T., Zheng, H., Guo, G.,
Hierarchical Pyramid Diverse Attention Networks for Face Recognition,
CVPR20(8323-8332)
IEEE DOI 2008
Face, Feature extraction, Face recognition, Handheld computers, Machine learning, Fuses, Computational modeling BibRef

Shi, Y., Yu, X., Sohn, K., Chandraker, M., Jain, A.K.,
Towards Universal Representation Learning for Deep Face Recognition,
CVPR20(6816-6825)
IEEE DOI 2008
Face recognition, Adaptation models, Training, Decorrelation, Prototypes, Training data, Correlation BibRef

Song, L., Gong, D., Li, Z., Liu, C., Liu, W.,
Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network,
ICCV19(773-782)
IEEE DOI 2004
convolutional neural nets, face recognition, feature extraction, hidden feature removal, image classification, Robustness BibRef

Balakrishnan, G., Dalca, A., Zhao, A., Guttag, J., Durand, F., Freeman, W.,
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions,
ICCV19(171-180)
IEEE DOI 2004
convolutional neural nets, face recognition, gait analysis, image classification, image motion analysis, image restoration, BibRef

Garg, S., Ramakrishnan, G., Thumbe, V.,
Interpretable Inference Graphs for Face Recognition,
IVCNZ19(1-6)
IEEE DOI 2004
convolutional neural nets, face recognition, feature extraction, graph theory, image classification, image colour analysis, Face Recognition BibRef

Wang, X., Wang, S., Shi, H., Wang, J., Mei, T.,
Co-Mining: Deep Face Recognition With Noisy Labels,
ICCV19(9357-9366)
IEEE DOI 2004
convolutional neural nets, data mining, face recognition, learning (artificial intelligence), loss values, noisy labels, Robustness BibRef

Chai, Z., Li, S., Meng, H., Lai, S., Wei, X., Zhang, J.,
A Progressive Learning Framework for Unconstrained Face Recognition,
LFR19(2703-2710)
IEEE DOI 2004
face recognition, learning (artificial intelligence), light weight backbone architecture, progressive learning, BibRef

Cheng, Y., Li, Y., Liu, Q., Yao, Y., Pedapudi, V.S.V.K., Fan, X., Su, C., Shen, S.,
A Graph Based Unsupervised Feature Aggregation for Face Recognition,
LFR19(2711-2720)
IEEE DOI 2004
directed graphs, face recognition, feature extraction, Gaussian distribution, graph theory, iterative methods, deep learning BibRef

Martindez-Díaz, Y., Luevano, L.S., Mendez-Vazquez, H., Nicolas-Diaz, M., Chang, L., Gonzalez-Mendoza, M.,
ShuffleFaceNet: A Lightweight Face Architecture for Efficient and Highly-Accurate Face Recognition,
LFR19(2721-2728)
IEEE DOI 2004
convolutional neural nets, face recognition, neural net architecture, parametric rectified linear unit, efficient architectures BibRef

Lyu, Y., Jiang, J., Zhang, K., Hua, Y., Cheng, M.,
Factorizing and Reconstituting Large-Kernel MBConv for Lightweight Face Recognition,
LFR19(2689-2697)
IEEE DOI 2004
face recognition, feature extraction, image reconstruction, neural nets, neural architecture search, NAS, Nueral Architecture Search BibRef

Li, X., Wang, F., Hu, Q., Leng, C.,
AirFace: Lightweight and Efficient Model for Face Recognition,
LFR19(2678-2682)
IEEE DOI 2004
convolutional neural nets, face recognition, learning (artificial intelligence), neural net architecture, loss function BibRef

Yu, H., Fan, Y., Chen, K., Yan, H., Lu, X., Liu, J., Xie, D.,
Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face Recognition,
LFR19(2662-2669)
IEEE DOI 2004
data handling, face recognition, image classification, image representation, labeled training dataset, known identities, Deep Convolutional Networks BibRef

Smirnov, E.[Evgeny], Oleinik, A.[Andrei], Lavrentev, A.[Aleksandr], Shulga, E.[Elizaveta], Galyuk, V.[Vasiliy], Garaev, N.[Nikita], Zakuanova, M.[Margarita], Melnikov, A.[Aleksandr],
Face Representation Learning using Composite Mini-Batches,
DFW19(551-559)
IEEE DOI 2004
face recognition, image representation, image sampling, interpolation, learning (artificial intelligence) BibRef

Awiszus, M., Ackermann, H., Rosenhahn, B.,
Learning Disentangled Representations via Independent Subspaces,
RSL-CV19(560-568)
IEEE DOI 2004
face recognition, image colour analysis, image segmentation, learning (artificial intelligence), neural nets, Latent Space Editing BibRef

Zee, T., Gali, G., Nwogu, I.,
Enhancing Human Face Recognition with an Interpretable Neural Network,
DFW19(514-522)
IEEE DOI 2004
convolutional neural nets, face recognition, feature extraction, similar-looking actresses, recognition task, Siamese networks BibRef

Chen, K.[Ken], Wu, Y.C.[Yi-Chao], Qin, H.Y.[Hao-Yu], Liang, D.[Ding], Liu, X.B.[Xue-Bo], Yan, J.J.[Jun-Jie],
R3 Adversarial Network for Cross Model Face Recognition,
CVPR19(9860-9868).
IEEE DOI 2002
BibRef

Zhao, K.[Kai], Xu, J.Y.[Jing-Yi], Cheng, M.M.[Ming-Ming],
RegularFace: Deep Face Recognition via Exclusive Regularization,
CVPR19(1136-1144).
IEEE DOI 2002
BibRef

Hu, W.[Wei], Huang, Y.Y.[Yang-Yu], Zhang, F.[Fan], Li, R.R.[Rui-Rui],
Noise-Tolerant Paradigm for Training Face Recognition CNNs,
CVPR19(11879-11888).
IEEE DOI 2002
BibRef

Zhong, Y.Y.[Yao-Yao], Deng, W.H.[Wei-Hong], Wang, M.[Mei], Hu, J.[Jiani], Peng, J.T.[Jian-Teng], Tao, X.Q.[Xun-Qiang], Huang, Y.H.[Yao-Hai],
Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data,
CVPR19(7804-7813).
IEEE DOI 2002
BibRef

Wang, Z.D.[Zhong-Dao], Zheng, L.[Liang], Li, Y.[Yali], Wang, S.J.[Sheng-Jin],
Linkage Based Face Clustering via Graph Convolution Network,
CVPR19(1117-1125).
IEEE DOI 2002
BibRef

Cirne, M., Andaló, F., Dias, R., Resek, T., Bertocco, G., Torres, R.d.S., Rocha, A.,
Deep Face Verification for Spherical Images,
ICIP19(3292-3296)
IEEE DOI 1910
Face verification, spherical images, convolutional neural networks, equirectangular projection BibRef

Wei, X., Wang, H., Scotney, B., Wan, H.,
Gicoface: Global Information-Based Cosine Optimal Loss for Deep Face Recognition,
ICIP19(3457-3461)
IEEE DOI 1910
Face recognition, Deep learning, CNNs, Loss function, Discriminative ability BibRef

Meng, X., Yan, Y., Chen, S., Wang, H.,
A Cascaded Noise-Robust Deep CNN for Face Recognition,
ICIP19(3487-3491)
IEEE DOI 1910
face recognition, image denoising, deep CNN, dense connectivity BibRef

Ardakani, P.B.[Parichehr B.], Velazquez, D.[Diego], Gonfaus, J.M.[Josep M.], Rodríguez, P.[Pau], Roca, F.X.[F. Xavier], Gonzàlez, J.[Jordi],
Catastrophic Interference in Disguised Face Recognition,
IbPRIA19(II:64-75).
Springer DOI 1910
Neural networks to completely and abruptly forget previously known information when learning new information. BibRef

Xu, T., Garrod, O., Scholte, S.H., Ince, R., Schyns, P.G.,
Using Psychophysical Methods to Understand Mechanisms of Face Identification in a Deep Neural Network,
Cognitive18(2057-20578)
IEEE DOI 1812
Face, Training, Lighting, Testing, Visualization, BibRef

Wang, H.[Hao], Wang, Y.T.[Yi-Tong], Zhou, Z.[Zheng], Ji, X.[Xing], Gong, D.H.[Di-Hong], Zhou, J.C.[Jing-Chao], Li, Z.F.[Zhi-Feng], Liu, W.[Wei],
CosFace: Large Margin Cosine Loss for Deep Face Recognition,
CVPR18(5265-5274)
IEEE DOI 1812
Face, Face recognition, Testing, Mars, Training, Feature extraction, Task analysis BibRef

Savchenko, A.,
Efficient Statistical Face Recognition Using Trigonometric Series and CNN Features,
ICPR18(3262-3267)
IEEE DOI 1812
Face recognition, Training, Complexity theory, Kernel, Feature extraction, Task analysis, Estimation BibRef

Kang, B.N.[Bong-Nam], Kim, Y.[Yonghyun], Kim, D.J.[Dai-Jin],
Pairwise Relational Networks for Face Recognition,
ECCV18(II: 646-663).
Springer DOI 1810
BibRef

Wang, Y.T.[Yi-Tong], Gong, D.H.[Di-Hong], Zhou, Z.[Zheng], Ji, X.[Xing], Wang, H.[Hao], Li, Z.F.[Zhi-Feng], Liu, W.[Wei], Zhang, T.[Tong],
Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition,
ECCV18(XV: 764-779).
Springer DOI 1810
BibRef

Rao, Q., Yu, B., Yang, Y., Feng, B.,
Knot Magnify Loss for Face Recognition,
ICIP18(2396-2400)
IEEE DOI 1809
Face, Training, Face recognition, Protocols, Feature extraction, Task analysis, Measurement, Deep convolutional neural networks, Quality imbalance BibRef

Barbosa Kloss, R., Jordao, A., Schwartz, W.R.,
Face Verification: Strategies for Employing Deep Models,
FG18(258-262)
IEEE DOI 1806
Computational modeling, Face, Feature extraction, Machine learning, Measurement, Standards, Task analysis, Artificial Neural Networks, Transfer learning BibRef

Qian, Y., Deng, W., Hu, J.,
Task Specific Networks for Identity and Face Variation,
FG18(271-277)
IEEE DOI 1806
Databases, Face, Face recognition, Feature extraction, Image reconstruction, Lighting, Task analysis, face recognition, task specific BibRef

Guo, G., Zhang, N.,
What Is the Challenge for Deep Learning in Unconstrained Face Recognition?,
FG18(436-442)
IEEE DOI 1806
Databases, Face, Face recognition, Image quality, Machine learning, Probes, Protocols, Deep Learning, Face recognition, challenge, unconstrained face recognition BibRef

Luo, Z., Hu, J., Deng, W., Shen, H.,
Deep Unsupervised Domain Adaptation for Face Recognition,
FG18(453-457)
IEEE DOI 1806
Databases, Face, Face recognition, Neural networks, Task analysis, Training, Training data, face recognition, unsupervised domain adaptation BibRef

Iqbal, A., Seghouane, A.K.,
An Approach for Sequential Dictionary Learning in Nonuniform Noise,
DICTA17(1-5)
IEEE DOI 1804
approximation theory, data analysis, face recognition, image coding, image denoising, image representation, Sparse matrices
See also Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis. BibRef

Su, C., Yan, Y., Chen, S., Wang, H.,
An efficient deep neural networks training framework for robust face recognition,
ICIP17(3800-3804)
IEEE DOI 1803
Complexity theory, Computer architecture, Convergence, Face, Face recognition, Neural networks, Training, Face recognition, triplet loss function BibRef

López-Avila, L.[Leyanis], Plasencia-Calaña, Y.[Yenisel], Martínez-Díaz, Y.[Yoanna], Méndez-Vázquez, H.[Heydi],
On the Use of Pre-trained Neural Networks for Different Face Recognition Tasks,
CIARP17(356-364).
Springer DOI 1802
BibRef

Neto, J.B.C.[João Baptista Cardia], Marana, A.N.[Aparecido Nilceu],
Utilizing Deep Learning and 3DLBP for 3D Face Recognition,
CIARP17(135-142).
Springer DOI 1802
BibRef

Gecer, B., Balntas, V., Kim, T.K.,
Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-Based Supervision,
AMFG17(1665-1672)
IEEE DOI 1802
Face, Face recognition, Magnetic losses, Support vector machines, Training BibRef

Hasnat, A., Bohné, J., Milgram, J., Gentric, S., Chen, L.,
DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,
AMFG17(1682-1691)
IEEE DOI 1802
Computational modeling, Convolution, Face, Face recognition, Training, Training data BibRef

Manmatha, R., Wu, C.Y., Smola, A.J.[Alexander J.], Krähenbühl, P.[Philipp],
Sampling Matters in Deep Embedding Learning,
ICCV17(2859-2867)
IEEE DOI 1802
face recognition, image retrieval, image sampling, learning (artificial intelligence), neural nets, Training BibRef

Ming, Z., Chazalon, J., Luqman, M.M., Visani, M., Burie, J.C.,
Simple Triplet Loss Based on Intra/Inter-Class Metric Learning for Face Verification,
AMFG17(1656-1664)
IEEE DOI 1802
Benchmark testing, Face, Face recognition, Feature extraction, Measurement, Training, Visualization BibRef

Han, B.B.[Bing-Bing], Zhang, Z.H.[Zhi-Hong], Xu, C.Y.[Chuan-Yu], Wang, B.Z.[Bei-Zhan], Hu, G.S.[Guo-Sheng], Bai, L.[Lu], Hong, Q.Q.[Qing-Qi], Hancock, E.R.[Edwin R.],
Deep Face Model Compression Using Entropy-Based Filter Selection,
CIAP17(II:127-136).
Springer DOI 1711
BibRef

Shen, H., Han, S., Philipose, M., Krishnamurthy, A.,
Fast Video Classification via Adaptive Cascading of Deep Models,
CVPR17(2197-2205)
IEEE DOI 1711
Adaptation models, Cameras, Face recognition, Motion pictures, Neural networks, Training BibRef

Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., van den Hengel, A.J.[Anton J.],
Sequential Person Recognition in Photo Albums with a Recurrent Network,
CVPR17(5660-5668)
IEEE DOI 1711
Context modeling, Face recognition, Predictive models, Training, Visualization BibRef

Kang, B.N., Kim, Y., Kim, D.,
Deep Convolutional Neural Network Using Triplets of Faces, Deep Ensemble, and Score-Level Fusion for Face Recognition,
Biometrics17(611-618)
IEEE DOI 1709
Bayes methods, Convolution, Databases, Feature extraction, IP networks, Image resolution, Training BibRef

Tripathi, S., Dane, G., Kang, B., Bhaskaran, V., Nguyen, T.,
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems,
ECVW17(411-420)
IEEE DOI 1709
Detectors, Face detection, Mathematical model, Object detection, Quantization (signal), Real-time systems, Training BibRef

Nakada, M., Wang, H., Terzopoulos, D.,
AcFR: Active Face Recognition Using Convolutional Neural Networks,
Cognition17(35-40)
IEEE DOI 1709
Computational modeling, Face, Face recognition, Feature extraction, Image recognition, Observers BibRef

Li, L., Jun, Z., Fei, J., Li, S.,
An incremental face recognition system based on deep learning,
MVA17(238-241)
DOI Link 1708
Data models, Face, Face recognition, Image resolution, Partitioning algorithms, Support vector machines, Training BibRef

Wan, L.H.[Li-Hong], Liu, N.[Na], Huo, H.[Hong], Fang, T.[Tao],
Face Recognition with Convolutional Neural Networks and subspace learning,
ICIVC17(228-233)
IEEE DOI 1708
Computer architecture, Databases, Face, Face recognition, Feature extraction, Principal component analysis, Training, convolutional neural networks, face recognition, linear discriminate analysis, whitening, principal, component, analysis BibRef