21.2.3 Face Recognition Systems Using Neural Networks, Learning

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

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.[Heesung], 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.[Heesung],
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.[Chaowei], Jin, Z.[Zhuoyi], Zhang, J.[Jinkun],
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

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

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.[Hongfu], 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

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

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

Iqbal, M.[Mansoor], Sameem, M.S.I.[M. Shujah Islam], Naqvi, N.[Nuzhat], Kanwal, S.[Shamsa], Ye, Z.F.[Zhong-Fu],
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

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

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

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
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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
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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

Low, C.Y., Teoh, A. .B.J.[A. Beng-Jin],
Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features,
ICIP17(1592-1596)
IEEE DOI 1803
Deep analytic network, face recognition, multi-fold filter convolution, object recognition, spectral histogram 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
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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
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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
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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, Computer vision, 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