22.2.9.4 Learning Face Detection, Neural Nets, SVM

Chapter Contents (Back)
Faces, Locating. Face Detection. Application, Faces. Learning. Neural Nets. Support Vector Machine.

Juell, P.[Paul], Marsh, R.[Ron],
A Hierarchical Neural-Network for Human Face Detection,
PR(29), No. 5, May 1996, pp. 781-787.
Elsevier DOI 9605
BibRef

Lin, S.H., Kung, S.Y., Lin, L.J.,
Face Recognition/Detection by Probabilistic Decision-Based Neural-Network,
TNN(8), No. 1, January 1997, pp. 114-132. 9701
BibRef

Kung, S.Y.[Sun-Yuan], Lin, S.H.[Shang-Hung], Lin, L.J.[Long-Ji], Fang, M.[Ming],
Neural network for locating and recognizing a deformable object,
US_Patent5,850,470, Dec 15, 1998
WWW Link. BibRef 9812

Kung, S.Y., Fang, M., Liou, S.P., Chiu, M.Y., Taur, J.S.,
Decision-based neural network for face recognition system,
ICIP95(I: 430-433).
IEEE DOI 9510
BibRef

Rowley, H.A.[Henry A.], Baluja, S.[Shumeet], Kanade, T.[Takeo],
Neural Network-Based Face Detection,
PAMI(20), No. 1, January 1998, pp. 23-38.
IEEE DOI 9803
BibRef
And: CVPR96(203-208).
IEEE DOI Award, Longuet-Higgins. Retinally connected NN looks at windows in the image. Uses multiple NNs. BibRef

Rowley, H.A.[Henry A.], Baluja, S.[Shumeet], Kanade, T.[Takeo],
Rotation Invariant Neural Network-based Face Detection,
CVPR98(38-44).
IEEE DOI BibRef 9800
And: CVPR98(963-963).
IEEE DOI Demo paper. BibRef
Earlier:
Human Face Detection in Visual Scenes,
CMU-CS-TR-95-158R, November 1995. (Revised Version)
PS File. BibRef

Baluja, S., Sahami, M., Rowley, H.A.,
Efficient face orientation discrimination,
ICIP04(I: 589-592).
IEEE DOI 0505
BibRef

Ng, J.[Jeffrey], Gong, S.G.[Shao-Gang],
Composite Support Vector Machines for Detection of Faces Across Views and Pose Estimation,
IVC(20), No. 5-6, 15 April 2002, pp. 359-368.
Elsevier DOI 0204
BibRef
Earlier:
Multi-View Face Detection and Pose Estimation Using a Composite Support Vector Machine across the View Sphere,
RATFG99(xx-yy).
See also Human Pose Estimation Using Structural Support Vector Machines. BibRef

Li, Y.M.[Yong-Min], Gong, S.G.[Shao-Gang], Sherrah, J.[Jamie], Liddell, H.[Heather],
Support Vector Machine Based Multi-View Face Detection and Recognition,
IVC(22), No. 5, 1 May 2004, pp. 413-427.
Elsevier DOI 0403

See also Human Pose Estimation Using Structural Support Vector Machines. BibRef

Li, Y.M.[Yong-Min], Gong, S.G.[Shao-Gang], Liddell, H.[Heather],
Support Vector Regression and Classification Based Multi-view Face Detection and Recognition,
AFGR00(300-305).
IEEE DOI 0003
BibRef

Kwong, J.N.S., Gong, S.G.,
Learning Support Vector Machines for A Multi-View Face Model,
BMVC99(Face Models and Recognition).
PDF File. BibRef 9900

Romdhani, S., Psarrou, A.[Alexandra], Gong, S.G.[Shao-Gang],
Learning a Single Active Face Shape Model across Views,
RATFG99(xx-yy). BibRef 9900

Huang, L.L.[Lin-Lin], Shimizu, A.[Akinobu], Hagihara, Y.[Yoshihoro], Kobatake, H.[Hidefumi],
Gradient feature extraction for classification-based face detection,
PR(36), No. 11, November 2003, pp. 2501-2511.
Elsevier DOI 0309
BibRef
Earlier:
Face Detection from Cluttered Images Using a Polynomial Neural Network,
ICIP01(II: 669-672).
IEEE DOI 0108
BibRef

Huang, L.L.[Lin-Lin], Shimizu, A.[Akinobu], Kobatake, H.[Hidefumi],
Robust face detection using Gabor filter features,
PRL(26), No. 11, August 2005, pp. 1641-1649.
Elsevier DOI 0506
BibRef
Earlier:
Classification-based face detection using Gabor filter features,
AFGR04(397-402).
IEEE DOI 0411
BibRef
Earlier:
Face detection using a modified radial basis function neural network,
ICPR02(II: 342-345).
IEEE DOI 0211
BibRef

Ko, J.[Jaepil], Byun, H.R.[Hye-Ran],
N-division output coding method applied to face recognition,
PRL(24), No. 16, December 2003, pp. 3115-3123.
Elsevier DOI 0310
BibRef
And:
Empirical remarks on output coding methods for face recognition,
AFGR04(333-338).
IEEE DOI 0411
BibRef

Ko, J.[Jaepil], Byun, H.R.[Hye-Ran],
Combining SVM Classifiers for Multiclass Problem: Its Application to Face Recognition,
AVBPA03(531-539).
Springer DOI 0310
BibRef
And:
Multi-class Support Vector Machines with Case-Based Combination for Face Recognition,
CAIP03(623-629).
Springer DOI 0311
BibRef

Lee, K.H.[Kyung-Hee], Chung, Y.W.[Yong-Wha], Byun, H.R.[Hye-Ran],
Face Recognition Using Support Vector Machines with the Feature Set Extracted by Genetic Algorithms,
AVBPA01(32).
Springer DOI 0310
BibRef

Liu, J.Z.[Jian-Zhuang], Tang, X.[Xiaoou],
Evolutionary search for faces from line drawings,
PAMI(27), No. 6, June 2005, pp. 861-872.
IEEE Abstract. 0506
BibRef
Earlier:
Efficient search of faces from complex line drawings,
CVPR04(II: 791-796).
IEEE DOI 0408
BibRef

Tang, X.[Xiaoou], Li, Z.F.[Zhi-Feng],
Audio-Guided Video-Based Face Recognition,
CirSysVideo(19), No. 7, July 2009, pp. 955-964.
IEEE DOI 0909
BibRef

Li, Z.F.[Zhi-Feng], Tang, X.[Xiaoou],
Bayesian face recognition using support vector machine and face clustering,
CVPR04(II: 374-380).
IEEE DOI 0408
BibRef

Tang, X.[Xiaoou], Li, Z.F.[Zhi-Feng],
Video based face recognition using multiple classifiers,
AFGR04(345-355).
IEEE DOI 0411
BibRef

Yan, S., Xu, D., Tang, X.,
Face Verification With Balanced Thresholds,
IP(16), No. 1, January 2007, pp. 262-268.
IEEE DOI 0701
BibRef

Xu, D., Yan, S., Luo, J.,
Face Recognition Using Spatially Constrained Earth Mover's Distance,
IP(17), No. 11, November 2008, pp. 1-5.
IEEE DOI 0810
BibRef

Huang, C.[Chang], Ai, H.Z.[Hai-Zhou], Li, Y.[Yuan], Lao, S.H.[Shi-Hong],
High-Performance Rotation Invariant Multiview Face Detection,
PAMI(29), No. 4, April 2007, pp. 671-686.
IEEE DOI 0703
BibRef
Earlier:
Learning Sparse Features in Granular Space for Multi-View Face Detection,
FGR06(401-407).
IEEE DOI 0604
BibRef
Earlier:
Vector Boosting for Rotation Invariant Multi-View Face Detection,
ICCV05(I: 446-453).
IEEE DOI 0510

See also Boosting Associated Pairing Comparison Features for pedestrian detection. BibRef

Li, Y.[Yuan], Huang, C.[Chang], Ai, H.Z.[Hai-Zhou],
Tsinghua Face Detection and Tracking for CLEAR 2007 Evaluation,
MTPH07(xx-yy).
Springer DOI 0705
BibRef

Huang, C.[Chang], Ai, H.Z.[Hai-Zhou], Wu, B.[Bo], Lao, S.H.[Shi-Hong],
Boosting nested cascade detector for multi-view face detection,
ICPR04(II: 415-418).
IEEE DOI 0409
BibRef
And: A3, A2, A1, A4:
Fast rotation invariant multi-view face detection based on real adaboost,
AFGR04(79-84).
IEEE DOI 0411
BibRef
Earlier: A1, A3, A2, A4:
Omni-directional face detection based on real adaboost,
ICIP04(I: 593-596).
IEEE DOI 0505
BibRef

Yao, B.P.[Bang-Peng], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
Person-specific face recognition in unconstrained environments: A combination of offline and online learning,
FG08(1-8).
IEEE DOI 0809
BibRef

Yao, B.P.[Bang-Peng], Ai, H.Z.[Hai-Zhou], Ijiri, Y.[Yoshihisa], Lao, S.H.[Shi-Hong],
Domain-Partitioning Rank-Boost for Face Recognition,
ICIP07(I: 129-132).
IEEE DOI 0709
BibRef

Ai, H.Z.[Hai-Zhou], Ying, L.H.[Li-Hang], Xu, G.Y.[Guang-You],
A subspace approach to face detection with support vector machines,
ICPR02(I: 45-48).
IEEE DOI 0211
BibRef
Earlier:
Face Detection Based on Template Matching and Support Vector Machines,
ICIP01(I: 1006-1009).
IEEE DOI 0108
BibRef

Meynet, J.[Julien], Popovici, V.[Vlad], Thiran, J.P.[Jean-Philippe],
Face detection with boosted Gaussian features,
PR(40), No. 8, August 2007, pp. 2283-2291.
Elsevier DOI 0704
Face detection; AdaBoost; Gaussian features BibRef

Meynet, J.[Julien], Popovici, V.[Vlad], Thiran, J.P.[Jean-Philippe],
Mixtures of boosted classifiers for frontal face detection,
SIViP(1), No. 1, April 2007, pp. 29-38.
Springer DOI 0706
BibRef

Antonini, G.[Gianluca], Popovici, V.[Vlad], Thiran, J.P.[Jean-Philippe],
Independent Component Analysis and Support Vector Machine for Face Feature Extraction,
AVBPA03(111-118).
Springer DOI 0310
BibRef

Meynet, J.[Julien], Popovici, V.[Vlad], Thiran, J.P.[Jean-Philippe],
Face Class Modeling Using Mixture of SVMs,
ICIAR04(II: 709-716).
Springer DOI 0409
BibRef

Popovici, V.[Vlad], Thiran, J.P.[Jean-Philippe],
Face Detection Using an SVM Trained in Eigenfaces Space,
AVBPA03(190-198).
Springer DOI 0310
BibRef

Qiu, G.P.[Guo-Ping], Fang, J.Z.[Jian-Zhong],
Classification in an informative sample subspace,
PR(41), No. 3, March 2008, pp. 949-960.
Elsevier DOI 0711
Information theory; Mutual information; Subspace methods; Representation; Classification; Object detection BibRef
Earlier: A2, A1:
Learning sample subspace with application to face detection,
ICPR04(II: 423-426).
IEEE DOI 0409
BibRef
Earlier: A2, A1:
Human face detection using angular radial transform and support vector machines,
ICIP03(I: 669-672).
IEEE DOI 0312
BibRef

Curran, K.[Kevin], McCaughley, N.[Neil], Li, X.L.[Xue-Long],
Addressing The Problems Of Detecting Faces With Neural Networks,
IJIG(7), No. 4, October 2007, pp. 617-640. 0710
BibRef

Hotta, K.[Kazuhiro],
Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel,
IVC(26), No. 11, 1 November 2008, pp. 1490-1498.
Elsevier DOI 0804
BibRef
Earlier:
Support Vector Machine with Weighted Summation Kernel Obtained by Adaboost,
AVSBS06(11-11).
IEEE DOI 0611
BibRef
Earlier:
Support vector machine with local summation kernel for robust face recognition,
ICPR04(III: 482-485).
IEEE DOI 0409
Support vector machine; Local kernel; Occlusion; Robust and face recognition
See also View independent face detection based on horizontal rectangular features and accuracy improvement using combination kernel of various sizes. BibRef

Hotta, K.[Kazuhiro],
Adaptive weighting of local classifiers by particle filters for robust tracking,
PR(42), No. 5, May 2009, pp. 619-628.
Elsevier DOI 0902
Adaptive; Weighting; Combination of local classifiers; Particle filter; Partial occlusion; Robust; Tracking BibRef

Hotta, K.[Kazuhiro],
Local normalized linear summation kernel for fast and robust recognition,
PR(43), No. 3, March 2010, pp. 906-913.
Elsevier DOI 1001
Local kernel; Normalized kernel; Summation kernel; Fast; Robust; Partial occlusion; Face detection; Face recognition BibRef

Yang, L.P.[Li-Ping], Gong, W.G.[Wei-Guo], Gu, X.H.[Xiao-Hua], Li, W.H.[Wei-Hong], Liu, Y.F.[Yan-Fei],
Bagging null space locality preserving discriminant classifiers for face recognition,
PR(42), No. 9, September 2009, pp. 1853-1858.
Elsevier DOI 0905
Locality preserving; Bagging; Discriminant analysis; Small sample size problem; Face recognition BibRef

Gu, X.H.[Xiao-Hua], Gong, W.G.[Wei-Guo], Yang, L.P.[Li-Ping], Li, W.H.[Wei-Hong],
Regularized Kernel Locality Preserving Discriminant Analysis for Face Recognition,
ACIVS10(II: 284-291).
Springer DOI 1012
BibRef

Yang, L.P.[Li-Ping], Gong, W.G.[Wei-Guo], Gu, X.H.[Xiao-Hua],
Extended Locality Preserving Discriminant Analysis for Face Recognition,
ICPR10(539-542).
IEEE DOI 1008
BibRef

Li, W.H.[Wei-Hong], Gong, W.G.[Wei-Guo], Yang, L.P.[Li-Ping], Chen, W.M.[Wei-Min], Gu, X.H.[Xiao-Hua],
Facial Feature Selection Based on SVMs by Regularized Risk Minimization,
ICPR06(III: 540-543).
IEEE DOI 0609
BibRef

Melin, P., Mendoza, O., Castillo, O.,
Face Recognition With an Improved Interval Type-2 Fuzzy Logic Sugeno Integral and Modular Neural Networks,
SMC-A(41), No. 5, September 2011, pp. 1001-1012.
IEEE DOI 1109
BibRef

Hou, X.N.[Xiao-Nan], Ding, S.H.[Shou-Hong], Ma, L.Z.[Li-Zhuang], Wang, C.J.[Cheng-Jie], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue],
Similarity metric learning for face verification using sigmoid decision function,
VC(32), No. 4, April 2016, pp. 479-490.
Springer DOI 1604
BibRef

Xu, L.[Lele], Wu, X.[Xia], Chen, K.W.[Ke-Wei], Yao, L.[Li],
Supervised within-class-similar discriminative dictionary learning for face recognition,
JVCIR(38), No. 1, 2016, pp. 561-572.
Elsevier DOI 1605
Discriminative dictionary learning BibRef

Wu, X.[Xia], Li, Q.[Qing], Xu, L.[Lele], Chen, K.W.[Ke-Wei], Yao, L.[Li],
Multi-Feature Kernel Discriminant Dictionary Learning for Face Recognition,
PR(66), No. 1, 2017, pp. 404-411.
Elsevier DOI 1704
Multi-feature kernel discriminative dictionary learning
See also Multi-Feature Kernel Discriminant Dictionary Learning for Classification in Alzheimer's Disease. BibRef

Rikhtegar, A., Pooyan, M., Manzuri-Shalmani, M.T.[Mohammad T.],
Genetic algorithm-optimised structure of convolutional neural network for face recognition applications,
IET-CV(10), No. 6, 2016, pp. 559-566.
DOI Link 1609
face recognition BibRef

Aslan, M.S.[Melih S.], Hailat, Z.[Zeyad], Alafif, T.K.[Tarik K.], Chen, X.W.[Xue-Wen],
Multi-channel multi-model feature learning for face recognition,
PRL(85), No. 1, 2017, pp. 79-83.
Elsevier DOI 1612
Unsupervised learning BibRef

Chen, Y.F.[Ye-Fei], Su, J.B.[Jian-Bo],
Sparse embedded dictionary learning on face recognition,
PR(64), No. 1, 2017, pp. 51-59.
Elsevier DOI 1701
Face recognition BibRef

Dong, X.[Xiao], Zhang, H.X.[Hua-Xiang], Sun, J.[Jiande], Wan, W.B.[Wen-Bo],
A two-stage learning approach to face recognition,
JVCIR(43), No. 1, 2017, pp. 21-29.
Elsevier DOI 1702
Collaborative representation BibRef

Zheng, J., Yang, P., Chen, S., Shen, G., Wang, W.,
Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning,
IP(26), No. 5, May 2017, pp. 2408-2423.
IEEE DOI 1704
Databases
See also Comments on Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning. BibRef

Pan, Y.Q.[Yu-Qi], Jiang, M.Y.[Ming-Yan],
LRR-TTK DL for face recognition,
IET-Bio(6), No. 3, May 2017, pp. 165-172.
DOI Link 1704
Low-Rank Representation based on Twin Tensor Kernel. Dictionary learning. BibRef

Fang, C.[Cong], Zhao, Z.Y.[Zhen-Yu], Zhou, P.[Pan], Lin, Z.C.[Zhou-Chen],
Feature learning via partial differential equation with applications to face recognition,
PR(69), No. 1, 2017, pp. 14-25.
Elsevier DOI 1706
Feature learning BibRef

Zhong, Y., Chen, J., Huang, B.,
Toward End-to-End Face Recognition Through Alignment Learning,
SPLetters(24), No. 8, August 2017, pp. 1213-1217.
IEEE DOI 1708
face recognition, learning (artificial intelligence), LFW dataset, alignment learning, automatic learning, end-to-end face recognition, face recognition methods, geometric transformations, human face structure, recognition feature extraction, Convolution, Face, Face detection, Face recognition, Feature extraction, Training, End-to-end (e2e) training, face alignment, face recognition, spatial, transformer BibRef

Li, H., Hu, H., Yip, C.,
Comments on 'Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning',
IP(26), No. 11, November 2017, pp. 5475-5476.
IEEE DOI 1709
Adaptation models, Convergence, Face recognition, Image reconstruction, Lagrangian functions, Probes, Xenon
See also Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning. BibRef

Lin, L.[Liang], Wang, K.[Keze], Meng, D.Y.[De-Yu], Zuo, W.M.[Wang-Meng], Zhang, L.[Lei],
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification,
PAMI(40), No. 1, January 2018, pp. 7-19.
IEEE DOI 1712
Face, Face recognition, Feature extraction, Neural networks, Noise measurement, Optimization, Training, Cost-effective model, self-paced learning
See also Joint Learning of Multiple Regressors for Single Image Super-Resolution. BibRef

Wang, F.Q.[Fa-Qiang], Zuo, W.M.[Wang-Meng], Lin, L.[Liang], Zhang, D.[David], Zhang, L.[Lei],
Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification,
CVPR16(1288-1296)
IEEE DOI 1612
BibRef

Cao, G.Q.[Guan-Qun], Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Neural class-specific regression for face verification,
IET-Bio(7), No. 1, January 2018, pp. 63-70.
DOI Link 1712
BibRef

Tran, D.T., Kiranyaz, S., Gabbouj, M., Iosifidis, A.,
Knowledge Transfer for Face Verification Using Heterogeneous Generalized Operational Perceptrons,
ICIP19(1168-1172)
IEEE DOI 1910
Face Verification, Generalized Operational Perceptron, Progressive Neural Network Learning BibRef

Zheng, L.[Lilei], Duffner, S., Idrissi, K., Garcia, C., Baskurt, A.,
Pairwise Identity Verification via Linear Concentrative Metric Learning,
Cyber(48), No. 1, January 2018, pp. 324-335.
IEEE DOI 1801
BibRef
Earlier: A1, A3, A4, A2, A5:
Triangular similarity metric learning for face verification,
FG15(1-7)
IEEE DOI 1508
Cost function, Data models, Face, Learning systems, Measurement, Training, Training data, Face verification, identity verification, speaker verification BibRef

Ranjan, R., Sankaranarayanan, S., Bansal, A., Bodla, N., Chen, J.C., Patel, V.M., Castillo, C.D., Chellappa, R.,
Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans,
SPMag(35), No. 1, January 2018, pp. 66-83.
IEEE DOI 1801
Biometrics, Detectors, Face recognition, Feature extraction, Graphics processing, Machine learning, Neural networks, Videos BibRef

Lu, Z.[Ze], Jiang, X.D.[Xu-Dong], Kot, A.C.[Alex C.],
Color space construction by optimizing luminance and chrominance components for face recognition,
PR(83), 2018, pp. 456-468.
Elsevier DOI 1808
BibRef
Earlier:
Enhance deep learning performance in face recognition,
ICIVC17(244-248)
IEEE DOI 1708
Color face recognition, Color space, Color sensor analysis, Chrominance subspace, Discriminant analysis, Covariance analysis. Databases, Face, Face recognition, Feature extraction, Image representation, Principal component analysis, Training, CNNs, VGG-face, fine-tuning, raw pixels BibRef

Banerjee, S., Das, S.,
LR-GAN for degraded Face Recognition,
PRL(116), 2018, pp. 246-253.
Elsevier DOI 1812
BibRef
Earlier:
Soft-Margin Learning for Multiple Feature-Kernel Combinations with Domain Adaptation, for Recognition in Surveillance Face Datasets,
Biometrics16(237-242)
IEEE DOI 1612
GAN, Face Recognition, DA, Nash equilibrium, PMSE, Jensen-Shannon divergence BibRef

Yu, B., Tao, D.,
Anchor Cascade for Efficient Face Detection,
IP(28), No. 5, May 2019, pp. 2490-2501.
IEEE DOI 1903
convolutional neural nets, face recognition, image classification, learning (artificial intelligence), cascade face detection BibRef

Villamizar, M.[Michael], Sanfeliu, A.[Alberto], Moreno-Noguer, F.[Francesc],
Online learning and detection of faces with low human supervision,
VC(35), No. 3, March 2019, pp. 349-370.
WWW Link. 1906
BibRef

Wu, W.Q.[Wen-Qi], Yin, Y.J.[Ying-Jie], Wang, X.G.[Xin-Gang], Xu, D.[De],
Face Detection With Different Scales Based on Faster R-CNN,
Cyber(49), No. 11, November 2019, pp. 4017-4028.
IEEE DOI 1908
convolutional neural nets, face recognition, feature extraction, learning (artificial intelligence), Faster R-CNN BibRef

Kefi-Fatteh, T.[Takoua], Ksantini, R.[Riadh], Kaâniche, M.B.[Mohamed-Bécha], Bouhoula, A.[Adel],
Human Face Detection Improvement Using Incremental Learning Based on Low Variance Directions,
SIViP(13), No. 8, November 2019, pp. 1503-1510.
Springer DOI 1911
BibRef
Earlier: ACIVS17(170-179).
Springer DOI 1712
BibRef

Massoli, F.V.[Fabio Valerio], Amato, G.[Giuseppe], Falchi, F.[Fabrizio],
Cross-resolution learning for Face Recognition,
IVC(99), 2020, pp. 103927.
Elsevier DOI 2006
Deep learning, Low resolution Face Recognition, Cross resolution Face Recognition BibRef

Zhou, Z.X.[Ze-Xun], He, Z.S.[Zhong-Shi], Jia, Y.Y.[Yuan-Yuan], Du, J.L.[Jing-Long], Wang, L.[Lulu], Chen, Z.Y.[Zi-Yu],
Context prior-based with residual learning for face detection: A deep convolutional encoder-decoder network,
SP:IC(88), 2020, pp. 115948.
Elsevier DOI 2009
Context prior, Residual learning, Deep convolutional neural network, Encoder-decoder, Face detection BibRef

Huang, Y.[Yuge], Shen, P.C.[Peng-Cheng], Tai, Y.[Ying], Li, S.X.[Shao-Xin], Liu, X.M.[Xiao-Ming], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue], Ji, R.R.[Rong-Rong],
Improving Face Recognition from Hard Samples via Distribution Distillation Loss,
ECCV20(XXX: 138-154).
Springer DOI 2010
BibRef

Huang, Y.H.[Yu-Hsuan], Chen, H.H.[Homer H.],
Deep face recognition for dim images,
PR(126), 2022, pp. 108580.
Elsevier DOI 2204
BibRef
Earlier:
Face Recognition Under Low Illumination Via Deep Feature Reconstruction Network,
ICIP20(2161-2165)
IEEE DOI 2011
Face recognition, Dim image, Rank-1 identification accuracy, Two-branch network, Convolutional neural network. illumination-invariant, illumination-robust, reconstruction network, illumination pre-processing. BibRef

Zhang, S.F.[Shi-Feng], Chi, C.[Cheng], Lei, Z.[Zhen], Li, S.Z.[Stan Z.],
RefineFace: Refinement Neural Network for High Performance Face Detection,
PAMI(43), No. 11, November 2021, pp. 4008-4020.
IEEE DOI 2110
Face, Detectors, Face detection, Feature extraction, Task analysis, Proposals, Neural networks, Face detection, refinement network, high performance BibRef

Yang, X.B.[Xian-Ben], Zhang, W.[Wei],
Heterogeneous face detection based on multi-task cascaded convolutional neural network,
IET-IPR(16), No. 1, 2022, pp. 207-215.
DOI Link 2112
BibRef

Chen, B.J.[Bei-Jing], Liu, X.[Xin], Zheng, Y.H.[Yu-Hui], Zhao, G.Y.[Guo-Ying], Shi, Y.Q.[Yun-Qing],
A Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception,
CirSysVideo(32), No. 6, June 2022, pp. 3527-3538.
IEEE DOI 2206
Faces, Feature extraction, Image color analysis, Convolution, Face detection, Robustness, Convolutional neural networks, color space BibRef

Zhou, J.C.[Jian-Can], Jia, X.[Xi], Li, Q.[Qiufu], Shen, L.L.[Lin-Lin], Duan, J.M.[Jin-Ming],
UniFace: Unified Cross-Entropy Loss for Deep Face Recognition,
ICCV23(20673-20682)
IEEE DOI 2401
BibRef


Jiang, C.C.[Chen-Chen], Ma, H.B.[Hong-Bing], Li, L.L.[Liang-Liang],
IRNet: An Improved RetinaNet Model for Face Detection,
ICIVC22(129-134)
IEEE DOI 2301
Training, Manuals, Feature extraction, Real-time systems, Hardware, Face detection, Faces, face detection, RetinaNet, feature fusion, weight decay BibRef

Liu, Y.[Yang], Wang, F.[Fei], Deng, J.K.[Jian-Kang], Zhou, Z.P.[Zhi-Peng], Sun, B.[Baigui], Li, H.[Hao],
MogFace: Towards a Deeper Appreciation on Face Detection,
CVPR22(4083-4092)
IEEE DOI 2210
Codes, Face recognition, Detectors, Object detection, Feature extraction, Face detection, Data mining, Face and gestures, retrieval BibRef

Liu, S.L.[Shi-Long], Zhang, L.[Lei], Yang, X.[Xiao], Su, H.[Hang], Zhu, J.[Jun],
Unsupervised Part Segmentation through Disentangling Appearance and Shape,
CVPR21(8351-8360)
IEEE DOI 2111
Geometry, Shape, Annotations, Face recognition, Semantics, Neural networks BibRef

Xiao, Z.H.[Zi-Hao], Gao, X.F.[Xian-Feng], Fu, C.L.[Chi-Lin], Dong, Y.P.[Yin-Peng], Gao, W.[Wei], Zhang, X.L.[Xiao-Lu], Zhou, J.[Jun], Zhu, J.[Jun],
Improving Transferability of Adversarial Patches on Face Recognition with Generative Models,
CVPR21(11840-11849)
IEEE DOI 2111
Manifolds, Face recognition, Perturbation methods, Authentication, Robustness, Security BibRef

Yang, X.[Xiao], Wei, F.Y.[Fang-Yun], Zhang, H.Y.[Hong-Yang], Zhu, J.[Jun],
Design and Interpretation of Universal Adversarial Patches in Face Detection,
ECCV20(XVII:174-191).
Springer DOI 2011
BibRef

Ghosh, S.S., Hua, Y., Mukherjee, S.S., Robertson, N.M.,
Improving Detection And Recognition Of Degraded Faces By Discriminative Feature Restoration Using GAN,
ICIP20(2146-2150)
IEEE DOI 2011
Face recognition, Faces, Facial features, Image recognition, Image restoration, CNN, GAN, detection, recognition, degraded face image BibRef

Zhu, J.S.[Jia-Shu], Li, D.[Dong], Han, T.T.[Tian-Tian], Tian, L.[Lu], Shan, Y.[Yi],
Progressface: Scale-aware Progressive Learning for Face Detection,
ECCV20(VI:344-360).
Springer DOI 2011
BibRef

Sanchez Tapia, L., Pattichis, M.S., Celedón-Pattichis, S., López Leiva, C.,
The Importance of the Instantaneous Phase for Face Detection using Simple Convolutional Neural Networks,
SSIAI20(1-4)
IEEE DOI 2009
channel bank filters, convolutional neural nets, face recognition, image filtering, image representation, low-complexity neural networks BibRef

Huang, Y., Wang, Y., Tai, Y., Liu, X., Shen, P., Li, S., Li, J., Huang, F.,
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition,
CVPR20(5900-5909)
IEEE DOI 2008
Training, Face recognition, Modulation, Convergence, Face, Adaptation models, Tuning BibRef

Winter, M.[Martin], Bailer, W.[Werner],
Incremental Training for Face Recognition,
MMMod19(I:289-299).
Springer DOI 1901
BibRef

Lezama, J., Qiu, Q., Musé, P., Sapiro, G.,
OLE: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning,
CVPR18(8109-8118)
IEEE DOI 1812
Training, Standards, Neural networks, Face, Measurement, Optimization BibRef

Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., Vetter, T.,
Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems,
AMFG18(2174-217409)
IEEE DOI 1812
Face, Face recognition, Lighting, Generators, Training data, Computer architecture, Task analysis BibRef

Siddiqui, S., Vatsa, M., Singh, R.,
Face Recognition for Newborns, Toddlers, and Pre-School Children: A Deep Learning Approach,
ICPR18(3156-3161)
IEEE DOI 1812
Pediatrics, Databases, Face, Face recognition, Feature extraction, Principal component analysis, Iris recognition BibRef

Jamal, M.A., Li, H., Gong, B.,
Deep Face Detector Adaptation Without Negative Transfer or Catastrophic Forgetting,
CVPR18(5608-5618)
IEEE DOI 1812
Detectors, Face, Training, Adaptation models, Cost function, Face detection BibRef

Song, G., Liu, Y., Jiang, M., Wang, Y., Yan, J., Leng, B.,
Beyond Trade-Off: Accelerate FCN-Based Face Detector with Higher Accuracy,
CVPR18(7756-7764)
IEEE DOI 1812
Face, Face detection, Proposals, Acceleration, Detectors, Estimation, Feature extraction BibRef

Le, C., Ma, H., Wang, X., Li, X.,
Key Parts Context and Scene Geometry in Human Head Detection,
ICIP18(1897-1901)
IEEE DOI 1809
Head, Detectors, Geometry, Task analysis, Training, Shape, Head detection, key parts context, scene geometry, convolutional neural network BibRef

Sun, Z., Peng, D., Cai, Z., Chen, Z., Jin, L.,
Scale Mapping and Dynamic Re-Detecting in Dense Head Detection,
ICIP18(1902-1906)
IEEE DOI 1809
Head, Proposals, Training, Task analysis, Testing, Object detection, Complexity theory, Object detection, convolutional neural network BibRef

Shi, W., Pattichis, M.S., Celedón-Pattichis, S., López Leiva, C.,
Robust Head Detection in Collaborative Learning Environments Using AM-FM Representations,
Southwest18(1-4)
IEEE DOI 1809
Hair, Frequency modulation, Face, Detectors, Robustness, Image color analysis, head detection, face detection, AM-FM representations BibRef

Chen, J., Lin, W., Zheng, J., Chellappa, R.,
A Real-Time Multi-Task Single Shot Face Detector,
ICIP18(176-180)
IEEE DOI 1809
Face, Task analysis, Detectors, Pose estimation, Face detection, face detection, fiducial detection, head pose estimation BibRef

Filali, H., Riffi, J., Mahraz, A.M., Tairi, H.,
Multiple face detection based on machine learning,
ISCV18(1-8)
IEEE DOI 1807
Gabor filters, face recognition, feature extraction, image classification, learning (artificial intelligence), Machine Learning BibRef

Nguyen-Meidine, L.T., Granger, E., Kiran, M., Blais-Morin, L.A.,
A comparison of CNN-based face and head detectors for real-time video surveillance applications,
IPTA17(1-7)
IEEE DOI 1804
computational complexity, face recognition, feedforward neural nets, image resolution, object detection, Video Surveillance BibRef

Zhang, H., Liu, H., Guo, D., Sun, F.,
From foot to head: Active face finding using deep Q-learning,
ICIP17(1862-1866)
IEEE DOI 1803
Cameras, Face, Learning (artificial intelligence), Manipulators, Testing, Training, Active face finding, Deep Q-learning BibRef

Zhang, G., Tu, E., Cui, D.,
Stable and improved generative adversarial nets (GANS): A constructive survey,
ICIP17(1871-1875)
IEEE DOI 1803
Convolution, Face, Generators, Pipelines, Task analysis, Training, GAN, generative adversarial networks, stable GAN BibRef

Hsu, G.S., Hsieh, C.H.,
Multi-dropout regression for wide-angle landmark localization,
ICIP17(3830-3834)
IEEE DOI 1803
Convolution, Databases, Detectors, Face, Face detection, Feature extraction, Training, Convolutional Neural Network, Face Alignment BibRef

Hu, G.S.[Guo-Sheng], Hua, Y.[Yang], Yuan, Y., Zhang, Z., Lu, Z., Mukherjee, S.S., Hospedales, T.M., Robertson, N.M.[Neil M.], Yang, Y.,
Attribute-Enhanced Face Recognition with Neural Tensor Fusion Networks,
ICCV17(3764-3773)
IEEE DOI 1802
face recognition, feature extraction, image fusion, learning (artificial intelligence), neural nets, optimisation, Tensile stress BibRef

Zhang, K., Zhang, Z., Wang, H., Li, Z., Qiao, Y., Liu, W.,
Detecting Faces Using Inside Cascaded Contextual CNN,
ICCV17(3190-3198)
IEEE DOI 1802
convolution, face recognition, image classification, image enhancement, learning (artificial intelligence), Training BibRef

Wang, J., Wang, B., Zheng, Y., Liu, W.,
Research and Implementation on Face Detection Approach Based on Cascaded Convolutional Neural Networks,
ICVISP17(34-39)
IEEE DOI 1712
Erbium, Signal processing, deep learning, face detection, multi-task learning BibRef

Khadhraoui, T.[Taher], Amiri, H.[Hamid],
Face Recognition with Single Training Sample per Subject,
CIAP17(II:202-212).
Springer DOI 1711
BibRef

Deng, J.K.[Jian-Kang], Zhou, Y.X.[Yu-Xiang], Zafeiriou, S.P.[Stefanos P.],
Marginal Loss for Deep Face Recognition,
FaceWild17(2006-2014)
IEEE DOI 1709
Databases, Face, Face recognition, Robustness, Standards, Training, Training, data BibRef

Mao, M.[Minqi], Zheng, Z.L.[Zhong-Long], Chen, Z.Y.[Zhong-Yu], Liu, H.W.[Hua-Wen], He, X.W.[Xiao-Wei], Ye, R.H.[Rong-Hua],
Group and collaborative dictionary pair learning for face recognition,
ICPR16(4107-4111)
IEEE DOI 1705
Algorithm design and analysis, Collaboration, Databases, Dictionaries, Encoding, Face recognition, Training, Collaborative, Dictionary Pair Learning, Face Recognition, Group BibRef

Yang, Z.[Zhenheng], Nevatia, R.,
A multi-scale cascade fully convolutional network face detector,
ICPR16(633-638)
IEEE DOI 1705
Detectors, Face, Face detection, Heating systems, Proposals, Streaming media, Training BibRef

Chariton, A.[Alexandros], Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Bag-Of-Features-Based Knowledge Distillation For Lightweight Convolutional Neural Networks,
ICIP22(1541-1545)
IEEE DOI 2211
Knowledge engineering, Convolution, Neural networks, Pipelines, Network architecture, Feature extraction, Convolutional Neural Networks BibRef

Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Learning Deep Representations with Probabilistic Knowledge Transfer,
ECCV18(XI: 283-299).
Springer DOI 1810
BibRef
Earlier:
Learning Discriminative Representations for Big Data Clustering Using Similarity-Based Dimensionality Reduction,
IVMSP18(1-5)
IEEE DOI 1809
BibRef
Earlier:
Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks,
ICCV17(5766-5774)
IEEE DOI 1802
Task analysis, Optimization, Clustering algorithms, Dimensionality reduction, Robustness, Clustering methods, Linear programming. convolution, feature extraction, image classification, image representation, Training BibRef

Tzelepi, M., Tefas, A.,
Fully Unsupervised Optimization of CNN Features Towards Content Based Image Retrieval,
IVMSP18(1-5)
IEEE DOI 1809
Task analysis, Image retrieval, Feature extraction, Adaptation models, Image representation, Computer architecture, Deep Learning BibRef

Triantafyllidou, D., Tefas, A.,
Face detection based on deep convolutional neural networks exploiting incremental facial part learning,
ICPR16(3560-3565)
IEEE DOI 1705
Computational modeling, Convolution, Detectors, Face, Face detection, Neural networks, Training BibRef

Guan, Y., Lu, T., Zhang, Y., Wang, B., Li, X., Xiong, Z.,
Efficient low-rank supported extreme learning machine for robust face recognition,
VCIP16(1-4)
IEEE DOI 1701
Algorithm design and analysis BibRef

Wang, K., Dong, Y., Bai, H., Zhao, Y., Hu, K.,
Use fast R-CNN and cascade structure for face detection,
VCIP16(1-4)
IEEE DOI 1701
Calibration BibRef

Vizilter, Y., Gorbatsevich, V., Vorotnikov, A., Kostromov, N.,
Real-Time Face Identification via CNN and Boosted Hashing Forest,
Biometrics16(146-154)
IEEE DOI 1612
BibRef

Ghazi, M.M., Ekenel, H.K.,
A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition,
Biometrics16(102-109)
IEEE DOI 1612
BibRef

Qin, H.W.[Hong-Wei], Yan, J.J.[Jun-Jie], Li, X.[Xiu], Hu, X.L.[Xiao-Lin],
Joint Training of Cascaded CNN for Face Detection,
CVPR16(3456-3465)
IEEE DOI 1612
BibRef

Li, Y.Z.[Yun-Zhu], Sun, B.Y.[Ben-Yuan], Wu, T.F.[Tian-Fu], Wang, Y.Z.[Yi-Zhou],
Face Detection with End-to-End Integration of a ConvNet and a 3D Model,
ECCV16(III: 420-436).
Springer DOI 1611
BibRef

Lin, S., Su, F.,
FCFD: Teach the machine to accomplish face detection step by step,
ICIP16(3214-3218)
IEEE DOI 1610
Detectors BibRef

Chowdhury, A.R., Lin, T.Y., Maji, S., Learned-Miller, E.G.[Erik G.],
One-to-many face recognition with bilinear CNNs,
WACV16(1-9)
IEEE DOI 1606
Benchmark testing BibRef

Iosifidis, A.[Alexandros], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine,
ICIP15(2449-2453)
IEEE DOI 1512
Approximate Methods BibRef

Li, H.X.[Hao-Xiang], Lin, Z.[Zhe], Shen, X.H.[Xiao-Hui], Brandt, J.[Jonathan], Hua, G.[Gang],
A convolutional neural network cascade for face detection,
CVPR15(5325-5334)
IEEE DOI 1510
BibRef

Shao, M.[Ming], Ding, Z.M.[Zheng-Ming], Fu, Y.[Yun],
Sparse low-rank fusion based deep features for missing modality face recognition,
FG15(1-6)
IEEE DOI 1508
face recognition Not all modalities in learning. BibRef

Thakur, S., Sing, J.K., Basu, D.K., Nasipuri, M.,
Face Recognition Using Posterior Distance Model Based Radial Basis Function Neural Networks,
PReMI09(470-475).
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Zhuang, S.S.[Shin-Shan], Lai, S.H.[Shang-Hong],
Face detection directly from h.264 compressed video with convolutional neural network,
ICIP09(2485-2488).
IEEE DOI 0911
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Zhang, Y.[Yan], Zhang, T.[Tao],
Combining Variation in the Bayesian Face Recognition,
CISP09(1-4).
IEEE DOI 0910
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Onis, S., Sanson, H., Garcia, C.,
Iterative unsupervised object detection system,
WSSIP08(397-400).
IEEE DOI 0806
Apply to faces. Give a few expamples, learn. BibRef

Tian, C.[Chunna], Fan, G.L.[Guo-Liang], Gao, X.B.[Xin-Bo],
Multi-view face recognition by nonlinear tensor decomposition,
ICPR08(1-4).
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Tian, C.[Chunna], Gao, X.B.[Xin-Bo], Li, J.[Jie],
A Valid Multi-View Face Detection Tree Based on Floatboost Learning,
ICIP06(2653-2656).
IEEE DOI 0610
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Bredin, H.[Herve], Dehak, N.[Najim], Chollet, G.[Gerard],
GMM-based SVM for face recognition,
ICPR06(III: 1111-1114).
IEEE DOI 0609
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He, X.G.[Xiao-Guang], Tian, J.[Jie], He, Y.L.[Yu-Liang], Yang, X.[Xin],
Face Recognition with Relative Difference Space and SVM,
ICPR06(III: 527-531).
IEEE DOI 0609
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Abeni, P., Baltatu, M.[Madalina], d'Alessandro, R.[Rosalia],
User Authentication based on Face Recognition with Support Vector Machines,
CRV06(42-42).
IEEE DOI 0607
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Gates, K.E.,
Fast and Accurate Face Recognition Using Support Vector Machines,
FRGC05(III: 163-163).
IEEE DOI 0507
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Jin, H.L.[Hong-Liang], Liu, Q.S.[Qing-Shan], Lu, H.Q.[Han-Qing],
Face detection using one-class-based support vectors,
AFGR04(457-462).
IEEE DOI 0411
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Chen, J.[Jie], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
Expand training set for face detection by GA re-sampling,
AFGR04(73-78).
IEEE DOI 0411
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And:
Resampling for face detection by self-adaptive genetic algorithm,
ICPR04(III: 822-825).
IEEE DOI 0409
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Fang, J., Qiu, G.,
Face Detection Based on Multiple Regression and Recognition Support Vector Machines,
BMVC03(xx-yy).
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Penev, P.S.[Penio S.],
Learning a Synchronous MAP for Improved Face Recognition,
LCV04(106).
IEEE DOI 0406
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Wu, Y.M.[Yi-Ming], Chan, K.L.[Kap Luk], Wang, L.[Lei],
Face recognition based on discriminative manifold learning,
ICPR04(IV: 171-174).
IEEE DOI 0409
BibRef

Cui, G.Q.[Guo-Qin], Gao, W.[Wen],
Support vector machines for face recognition with two-layer generated virtual data,
ICPR04(III: 570-573).
IEEE DOI 0409
BibRef

Sun, J.[Jie], Rehg, J.M., Bobick, A.F.,
Automatic cascade training with perturbation bias,
CVPR04(II: 276-283).
IEEE DOI 0408
Cascade learning for face detection. BibRef

Fransens, R., DePrins, J., Van Gool, L.J.,
SVM-based nonparametric discriminant analysis, an application to face detection,
ICCV03(1289-1296).
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Zhang, B.L.[Bai-Ling], Guo, Y.[Yan],
Face Recognition by Auto-associative Radial Basis Function Network,
AVBPA01(52).
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Ma, Y.[Yong], Ding, X.Q.[Xiao-Qing],
Face detection based on hierarchical support vector machines,
ICPR02(I: 222-225).
IEEE DOI 0211
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Ma, Y.[Yong], Ding, X.Q.[Xiao-Qing],
Real-time rotation invariant face detection based on cost-sensitive AdaBoost,
ICIP03(III: 921-924).
IEEE DOI 0312
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Kim, T.K.[Tae-Kyun], Kong, D.G.[Dong-Geon], Kim, S.R.[Sang-Ryong],
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ICIP02(I: 920-923).
IEEE DOI 0210
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Sahbi, H., Boujemaa, N.,
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IEEE DOI 0211
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Sahbi, H., Boujemaa, N.,
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ICIP01(I: 1010-1013).
IEEE DOI 0108
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Sahbi, H., Geman, D., Boujemaa, N.,
Face detection using coarse-to-fine support vector classifiers,
ICIP02(III: 925-928).
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Frontal Face Detection Using Support Vector Machines and Back-Propagation Neural Networks,
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ICIP01(I: 1002-1005).
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Learning the Human Face Concept from Black and White Images,
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Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Face Detection in Video .


Last update:Mar 16, 2024 at 20:36:19