21.2.3 Face Recognition Systems Using Neural Networks, Learning

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

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., Liu, X.,
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., Yin, X., Liu, X.,
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition,
CVPR17(1283-1292)
IEEE DOI 1711
Decoding, Face, Face recognition, Gallium nitride, 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

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

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

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
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

Wu, W., Kan, M., Liu, X., Yang, Y., Shan, S., Chen, X.,
Recursive Spatial Transformer (ReST) for Alignment-Free Face Recognition,
ICCV17(3792-3800)
IEEE DOI 1802
face recognition, feature extraction, learning (artificial intelligence), neural nets, CNN, Transforms 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

Ferrari, C., Lisanti, G., Berretti, S., del Bimbo, A.[Alberto],
Investigating Nuisance Factors in Face Recognition with DCNN Representation,
Biometrics17(583-591)
IEEE DOI 1709
Computer architecture, Computer vision, Face, Face recognition, Feature extraction, Machine learning, 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

Dong, B.[Bin], An, Z.F.[Zhan-Fu], Lin, J.[Jian], Deng, W.H.[Wei-Hong],
Attention-Based Template Adaptation for Face Verification,
FG17(941-946)
IEEE DOI 1707
Face, Feature extraction, Neural networks, Probabilistic logic, Three-dimensional displays, Training, Videos BibRef

Franc, V.[Vojtech], Cech, J.[Jan],
Learning CNNs for Face Recognition from Weakly Annotated Images,
FG17(933-940)
IEEE DOI 1707
Convolution, Databases, Detectors, Estimation, Face, Face, recognition BibRef

Parde, C.J.[Connor J.], Castillo, C.[Carlos], Hill, M.Q.[Matthew Q.], Colon, Y.I.[Y. Ivette], Sankaranarayanan, S.[Swami], Chen, J.C.[Jun-Cheng], O'Toole, A.J.[Alice J.],
Face and Image Representation in Deep CNN Features,
FG17(673-680)
IEEE DOI 1707
Face, Face recognition, Feature extraction, Media, Metadata, Robustness, Training BibRef

He, Z., Zhang, J., Kan, M., Shan, S., Chen, X.,
Robust FEC-CNN: A High Accuracy Facial Landmark Detection System,
FaceWild17(2044-2050)
IEEE DOI 1709
BibRef
And: A1, A3, A2, A5, A4:
A Fully End-to-End Cascaded CNN for Facial Landmark Detection,
FG17(200-207)
IEEE DOI 1707
Face, Feature extraction, Neural networks, Robustness, Shape, Silicon, Training Mathematical model, Predictive models, Testing BibRef

Narang, N.[Neeru], Martin, M., Metaxas, D.N., Bourlai, T.[Thirimachos],
Learning Deep Features for Hierarchical Classification of Mobile Phone Face Datasets in Heterogeneous Environments,
FG17(186-193)
IEEE DOI 1707
Cameras, Databases, Face, Face detection, Mobile handsets, Pose estimation, Videos BibRef

Peng, X., Ratha, N., Pankanti, S.,
Learning face recognition from limited training data using deep neural networks,
ICPR16(1442-1447)
IEEE DOI 1705
Face, Face recognition, Feature extraction, Image recognition, Machine learning, Training, Training, data BibRef

Mygdalis, V., Tefas, A., Pitas, I.,
Exploiting local and global geometric data relationships in Support Vector Data Description,
ICPR16(515-519)
IEEE DOI 1705
Data models, Face recognition, Kernel, Optimization, Support vector machines, Training, Training, data BibRef

Baumgartner, T.[Tobi], Culpepper, J.[Jack],
Deep Architectures for Face Attributes,
WFI16(II: 334-344).
Springer DOI 1704
BibRef

Wang, S.[Shiyao], Deng, Z.D.[Zhi-Dong], Wang, Z.Y.[Zhen-Yang],
Collaborative Learning Network for Face Attribute Prediction,
ACCV16(III: 361-374).
Springer DOI 1704
BibRef

Liu, H.[Hao], Duan, H.P.[Hui-Ping], Cui, H.Y.[Hong-Yu], Yin, Y.J.[Yun-Jie],
Face recognition using training data with artificial occlusions,
VCIP16(1-4)
IEEE DOI 1701
Databases. For criminal id. BibRef

Opitz, M.[Michael], Waltner, G.[Georg], Poier, G.[Georg], Possegger, H.[Horst], Bischof, H.[Horst],
Grid Loss: Detecting Occluded Faces,
ECCV16(III: 386-402).
Springer DOI 1611
Grid loss layer for CNN to deal with occlusions. BibRef

Zhang, X., Fang, Z., Wen, Y., 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 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

Saxena, S.[Shreyas], Verbeek, J.[Jakob],
Heterogeneous Face Recognition with CNNs,
TASKCV16(III: 483-491).
Springer DOI 1611
BibRef

Zhang, T.[Ting], Dong, Q.L.[Qiu-Lei], Hu, Z.Y.[Zhan-Yi],
Pursuing face identity from view-specific representation to view-invariant representation,
ICIP16(3244-3248)
IEEE DOI 1610
Brain modeling BibRef

Wu, R., Kamata, S.I.,
A jointly local structured sparse deep learning network for face recognition,
ICIP16(3026-3030)
IEEE DOI 1610
Databases BibRef

Grundström, J.[Jakob], Chen, J.[Jiandan], Ljungqvist, M.G.[Martin Georg], Åström, K.[Kalle],
Transferring and Compressing Convolutional Neural Networks for Face Representations,
ICIAR16(20-29).
Springer DOI 1608
BibRef

Sudowe, P., Spitzer, H., Leibe, B.,
Person Attribute Recognition with a Jointly-Trained Holistic CNN Model,
ChaLearnDec15(329-337)
IEEE DOI 1602
Benchmark testing BibRef

Hu, G., Yang, Y., Yi, D., Kittler, J.V., Christmas, W., Li, S.Z., Hospedales, T.M.,
When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition,
ChaLearnDec15(384-392)
IEEE DOI 1602
Convolutional codes BibRef

Dam, N.[Nhan], Nguyen, V.T.[Vinh-Tiep], Do, M.N.[Minh N.], Duong, A.D.[Anh-Duc], Tran, M.T.[Minh-Triet],
Realtime Face Verification with Lightweight Convolutional Neural Networks,
ISVC15(II: 420-430).
Springer DOI 1601
BibRef

Ma, Y.K.[Yu-Kun], He, J.Y.[Jiao-Yu], Wu, L.F.[Li-Fang], Qi, W.[Wei],
An Effective Face Verification Algorithm to Fuse Complete Features in Convolutional Neural Network,
MMMod16(II: 39-46).
Springer DOI 1601
BibRef

Liu, S.[Sifei], Yang, J.[Jimei], Huang, C.[Chang], Yang, M.H.[Ming-Hsuan],
Multi-objective convolutional learning for face labeling,
CVPR15(3451-3459)
IEEE DOI 1510
BibRef

Sun, Y.[Yi], Wang, X.G.[Xiao-Gang], Tang, X.[Xiaoou],
Deeply learned face representations are sparse, selective, and robust,
CVPR15(2892-2900)
IEEE DOI 1510
BibRef

Lo, H.Z., Cohen, J.P., Ding, W.[Wei],
Prediction gradients for feature extraction and analysis from convolutionalat neural networks,
FG15(1-6)
IEEE DOI 1508
computer vision BibRef

Tsai, Y.H.[Yao-Hung], Hsu, H.M.[Hung-Ming], Hou, C.A.[Cheng-An], Wang, Y.C.F.[Yu-Chiang Frank],
Person-specific domain adaptation with applications to heterogeneous face recognition,
ICIP14(338-342)
IEEE DOI 1502
Adaptation models BibRef

Hou, C.A.[Cheng-An], Yang, M.C.[Min-Chun], Wang, Y.C.F.[Yu-Chiang Frank],
Domain Adaptive Self-Taught Learning for Heterogeneous Face Recognition,
ICPR14(3068-3073)
IEEE DOI 1412
Dictionaries BibRef

Agarwal, V., Bhanot, S.,
Evolutionary design of Multiquadric radial basis functions neural network for face recognition,
NCVPRIPG13(1-5)
IEEE DOI 1408
evolutionary computation BibRef

Barreto, R.M.[Rafael M.], Ren, T.I.[Tsang Ing], Cavalcanti, G.D.C.[George D. C.],
L2-Norm metric learning applied to unconstrained face pair-matching,
ICIP12(581-584).
IEEE DOI 1302
BibRef

Dragoni, A.F.[Aldo Franco], Vallesi, G.[Germano], Baldassarri, P.[Paola],
A Continuous Learning in a Changing Environment,
CIAP11(II: 79-88).
Springer DOI 1109
Combine multiple Neural networks with Bayes rule for face recognition. BibRef

Ren, Y.[Yong], Iftekharuddin, K.M.[Khan M.], White, W.E.[William E.],
Recurrent network-based face recognition using image sequences,
CIMSVP09(41-46).
IEEE DOI 0903
BibRef

Gharai, S., Thakur, S., Lahiri, S., Sing, J.K., Basu, D.K., Nasipuri, M., Kundu, M.,
Self-adaptive RBF Neural Networks for Face Recognition,
ISVC06(I: 353-362).
Springer DOI 0611
BibRef

Wang, Y.H.[Yun-Hong], Wang, Y.D.[Yi-Ding], Jain, A.K.[Anil K.], Tan, T.N.[Tie-Niu],
Face Verification Based on Bagging RBF Networks,
ICB06(69-77).
Springer DOI 0601
BibRef

Kurita, T., Pic, M., Takahashi, T.,
Recognition and detection of occluded faces by a neural network classifier with recursive data reconstruction,
AVSBS03(53-58).
IEEE DOI 0310
BibRef

Singh, R.K., Rajagopalan, A.N.,
Background learning for robust face recognition,
ICPR02(III: 525-528).
IEEE DOI 0211
BibRef

Fasel, B.,
Robust face analysis using convolutional neural networks,
ICPR02(II: 40-43).
IEEE DOI 0211
BibRef

Pujol, A., Wechsler, H., Villanueva, J.J.,
Learning and caricaturing the face space using self-organization and Hebbian learning for face processing,
CIAP01(273-278).
IEEE DOI 0210
BibRef

Howell, A.J.[A. Jonathan], Buxton, H.[Hilary],
Towards unconstrained face recognition from image sequences,
AFGR96(224-229).
IEEE DOI 9610
BibRef
And:
Face Recognition using Radial Basis Function Neural Networks,
BMVC96(Poster Session 2). 9608
University of Sussex BibRef

Duvdevani-Bar, S., Edelman, S., Howell, A.J.[A. Jonathan], Buxton, H.[Hilary],
Similarity-Based Method for the Generalization of Face Recognition over Pose and Expression,
AFGR98(118-123).
IEEE DOI BibRef 9800

Kerin, M.A., Stonham, T.J.,
Face recognition using a digital neural network with self-organising capabilities,
ICPR90(I: 738-741).
IEEE DOI 9006
BibRef

Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Face Analysis, Profiles .