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A neural architecture for fast and robust face detection,
ICPR02(II: 44-47).
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Rotated 20deg, turned 60deg.
Learn features extractors for recognition.
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0808
View-independent face recognition; Mixture of experts;
Teacher-directed learning; Single-view eigenspaces;
Global eigenspace; Overlapping eigenspaces
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channel bank filters
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CVPR13(708-715)
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1309
low rank; partial permutation matrix; weakly supervised learning
Faces with partial captions.
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Convolutional Fusion Network for Face Verification in the Wild,
CirSysVideo(26), No. 3, March 2016, pp. 517-528.
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1603
Accuracy
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Conditional Convolutional Neural Network for Modality-Aware Face
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ICCV15(3667-3675)
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1602
Convolution
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1603
Convolutional architectures
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SP:IC(47), No. 1, 2016, pp. 465-475.
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Feature representation
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PR(66), No. 1, 2017, pp. 63-73.
Elsevier DOI
1704
Fine-grained visual recognition.
Includes a fine-grained dataset.
See also Lighting-aware face frontalization for unconstrained face recognition.
See also Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach.
BibRef
Reale, C.[Christopher],
Lee, H.[Hyungtae],
Kwon, H.S.[Hee-Sung],
Chellappa, R.[Rama],
Deep Network Shrinkage Applied to Cross-Spectrum Face Recognition,
FG17(897-903)
IEEE DOI
1707
Ad hoc networks, Convolution, Face, Face recognition,
Machine learning, Optimization, Training
BibRef
Reale, C.[Christopher],
Lee, H.[Hyungtae],
Kwon, H.S.[Hee-Sung],
Deep Heterogeneous Face Recognition Networks Based on Cross-Modal
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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
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SPLetters(24), No. 11, November 2017, pp. 1581-1585.
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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
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JVCIR(55), 2018, pp. 548-560.
Elsevier DOI
1809
Orthogonal filters, Region covariance matrices, Log-TiedRank, Face recognition
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Hu, G.,
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Hospedales, T.M.,
Verbeek, J.[Jakob],
Frankenstein: Learning Deep Face Representations Using Small Data,
IP(27), No. 1, January 2018, pp. 293-303.
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1712
data analysis, face recognition, image representation,
learning (artificial intelligence), neural nets,
small training data
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Yin, X.[Xi],
Liu, X.M.[Xiao-Ming],
Multi-Task Convolutional Neural Network for Pose-Invariant Face
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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
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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
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Al-Waisy, A.S.[Alaa S.],
Qahwaji, R.[Rami],
Ipson, S.[Stanley],
Al-Fahdawi, S.[Shumoos],
A multimodal deep learning framework using local feature
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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
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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
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PR(80), 2018, pp. 225-240.
Elsevier DOI
1805
Transfer learning, Facial attribute classification,
Multi-label learning, Deep learning, Convolutional neural networks
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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
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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
Zhao, J.[Jian],
Xing, J.L.[Jun-Liang],
Xiong, L.[Lin],
Yan, S.C.[Shui-Cheng],
Feng, J.S.[Jia-Shi],
Recognizing Profile Faces by Imagining Frontal View,
IJCV(128), No. 2, February 2020, pp. 460-478.
Springer DOI
2002
BibRef
Wei, X.[Xin],
Wang, H.[Hui],
Scotney, B.W.[Bryan W.],
Wan, H.[Huan],
Minimum Margin Loss for Deep Face Recognition,
PR(97), 2020, pp. 107012.
Elsevier DOI
1910
BibRef
And:
Precise Adjacent Margin Loss for Deep Face Recognition,
ICIP19(3641-3645)
IEEE DOI
1910
Deep learning, Convolutional neural networks, Face recognition,
Minimum margin loss. Margin, Loss, Deep learning.
BibRef
Zhang, L.,
Liu, J.,
Zhang, B.,
Zhang, D.,
Zhu, C.,
Deep Cascade Model-Based Face Recognition:
When Deep-Layered Learning Meets Small Data,
IP(29), No. , 2020, pp. 1016-1029.
IEEE DOI
1911
Image coding, Encoding, Face recognition,
Nuclear magnetic resonance, Deep learning, Data models,
corruption
BibRef
Ding, Z.,
Shao, M.,
Hwang, W.,
Suh, S.,
Han, J.,
Choi, C.,
Fu, Y.,
Robust Discriminative Metric Learning for Image Representation,
CirSysVideo(29), No. 11, November 2019, pp. 3173-3183.
IEEE DOI
1911
Measurement, Data models, Noise reduction, Optimization,
Face recognition, Feature extraction,
fast low-rank representation
BibRef
Wang, Q.C.[Qiang-Chang],
Guo, G.D.[Guo-Dong],
Benchmarking deep learning techniques for face recognition,
JVCIR(65), 2019, pp. 102663.
Elsevier DOI
1912
Deep learning, Convolutional neural networks, Face recognition,
GPU, PyTorch, TensorFlow, Caffe, AlexNet, ArcFace, Center-loss, VGG
BibRef
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
Zou, F.H.[Fu-Hao],
Yang, F.[Fan],
Chen, W.[Wei],
Li, K.[Kai],
Song, J.K.[Jing-Kuan],
Chen, J.C.[Jing-Cai],
Ling, H.[Hefei],
Fast large scale deep face search,
PRL(130), 2020, pp. 83-90.
Elsevier DOI
2002
Face recognition, Semantic hashing, Deep convolution neural network,
Face search
BibRef
Iranmanesh, S.M.[Seyed Mehdi],
Riggan, B.[Benjamin],
Hu, S.[Shuowen],
Nasrabadi, N.M.[Nasser M.],
Coupled generative adversarial network for heterogeneous face
recognition,
IVC(94), 2020, pp. 103861.
Elsevier DOI
2003
Heterogeneous face recognition,
Generative adversarial networks, Face verification, Biometrics
BibRef
Peixoto, S.A.[Solon A.],
Vasconcelos, F.F.X.[Francisco F.X.],
Guimarães, M.T.[Matheus T.],
Medeiros, A.G.[Aldísio G.],
Rego, P.A.L.[Paulo A.L.],
Lira Neto, A.V.[Aloísio V.],
de Albuquerque, V.H.C.[Victor Hugo C.],
Rebouças Filho, P.P.[Pedro P.],
A high-efficiency energy and storage approach for IoT applications of
facial recognition,
IVC(96), 2020, pp. 103899.
Elsevier DOI
2005
Data compression, Face recognition, IoT, Deep learning
BibRef
Liu, L.[Li],
Chen, S.[Siqi],
Chen, X.X.[Xiu-Xiu],
Wang, T.S.[Tian-Shi],
Zhang, L.[Long],
Fuzzy weighted sparse reconstruction error-steered semi-supervised
learning for face recognition,
VC(36), No. 8, August 2020, pp. 1521-1534.
WWW Link.
2007
BibRef
Pernici, F.[Federico],
Bruni, M.[Matteo],
del Bimbo, A.[Alberto],
Self-supervised on-line cumulative learning from video streams,
CVIU(197-198), 2020, pp. 102983.
Elsevier DOI
2008
Incremental Learning, Cumulative Learning,
Memory Based Learning, Multiple Object Tracking,
Long Term Object Tracking
BibRef
Pernici, F.[Federico],
Bartoli, F.[Federico],
Bruni, M.[Matteo],
del Bimbo, A.[Alberto],
Memory Based Online Learning of Deep Representations from Video
Streams,
CVPR18(2324-2334)
IEEE DOI
1812
Face, Streaming media, Memory modules, Object tracking,
Graphics processing units, Visualization, Learning systems
BibRef
Liu, Y.F.[Yan-Fei],
Chen, J.H.[Jun-Hua],
Qiu, Y.[Yu],
Joint Multi-Patch and Multi-Task CNNs for Robust Face Recognition,
IEICE(E103-D), No. 10, October 2020, pp. 2178-2187.
WWW Link.
2010
BibRef
He, R.[Ran],
Li, Y.[Yi],
Wu, X.[Xiang],
Song, L.X.[Ling-Xiao],
Chai, Z.H.[Zhen-Hua],
Wei, X.L.[Xiao-Lin],
Coupled adversarial learning for semi-supervised heterogeneous face
recognition,
PR(110), 2021, pp. 107618.
Elsevier DOI
2011
Adversarial learning, Heterogeneous face recognition, Deep representation
BibRef
Zhu, Y.H.[Ying-Hui],
Jiang, Y.Z.[Yu-Zhen],
Optimization of face recognition algorithm based on deep learning
multi feature fusion driven by big data,
IVC(104), 2020, pp. 104023.
Elsevier DOI
2012
Big data, Face recognition, Deep learning, Multi-feature fusion
BibRef
Massoli, F.V.[Fabio Valerio],
Falchi, F.[Fabrizio],
Amato, G.[Giuseppe],
Cross-resolution face recognition adversarial attacks,
PRL(140), 2020, pp. 222-229.
Elsevier DOI
2012
Deep learning, Face recognition, Adversarial attacks,
Face identification, Adversarial biometrics
BibRef
Low, C.Y.,
Park, J.,
Teoh, A. .B.J.[A. Beng-Jin],
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern
Classification,
Cyber(50), No. 12, December 2020, pp. 5021-5034.
IEEE DOI
2012
BibRef
Earlier: A1, A3, Only:
Stacking-based deep neural network:
Deep analytic network on convolutional spectral histogram features,
ICIP17(1592-1596)
IEEE DOI
1803
Kernel, Training, Neural networks, Graphics processing units,
Principal component analysis, Convolution, Cybernetics,
stacking-based deep neural network (S-DNN).
Deep analytic network, face recognition,
multi-fold filter convolution, object recognition, spectral histogram
BibRef
Feng, Y.S.[Yu-Shu],
Wang, H.[Huan],
Hu, H.J.R.[Hao-Ji Roland],
Yu, L.[Lu],
Wang, W.[Wei],
Wang, S.Y.[Shi-Yan],
Triplet Distillation For Deep Face Recognition,
ICIP20(808-812)
IEEE DOI
2011
Face Recognition, Knowledge Distillation, Triplet Loss, Network Compression
BibRef
Kim, Y.,
Park, W.,
Roh, M.,
Shin, J.,
GroupFace: Learning Latent Groups and Constructing Group-Based
Representations for Face Recognition,
CVPR20(5620-5629)
IEEE DOI
2008
Face recognition, Face, Machine learning, Training, Measurement,
Labeling, Computational modeling
BibRef
Yu, H.,
Zheng, W.,
Weakly Supervised Discriminative Feature Learning With State
Information for Person Identification,
CVPR20(5527-5537)
IEEE DOI
2008
Cameras, Task analysis, Visualization, Distortion, Face recognition,
Scalability, Training
BibRef
Chang, J.,
Lan, Z.,
Cheng, C.,
Wei, Y.,
Data Uncertainty Learning in Face Recognition,
CVPR20(5709-5718)
IEEE DOI
2008
Uncertainty, Face, Face recognition, Noise measurement, Data models,
Training, Gaussian distribution
BibRef
Zhang, Y.,
Deng, W.,
Class-Balanced Training for Deep Face Recognition,
Biometrics20(3594-3603)
IEEE DOI
2008
Training, Face recognition, Face, Benchmark testing, Data structures,
Data models
BibRef
Wang, Q.,
Wu, T.,
Zheng, H.,
Guo, G.,
Hierarchical Pyramid Diverse Attention Networks for Face Recognition,
CVPR20(8323-8332)
IEEE DOI
2008
Face, Feature extraction, Face recognition, Handheld computers,
Machine learning, Fuses, Computational modeling
BibRef
Shi, Y.,
Yu, X.,
Sohn, K.,
Chandraker, M.,
Jain, A.K.,
Towards Universal Representation Learning for Deep Face Recognition,
CVPR20(6816-6825)
IEEE DOI
2008
Face recognition, Adaptation models, Training, Decorrelation,
Prototypes, Training data, Correlation
BibRef
Song, L.,
Gong, D.,
Li, Z.,
Liu, C.,
Liu, W.,
Occlusion Robust Face Recognition Based on Mask Learning With
Pairwise Differential Siamese Network,
ICCV19(773-782)
IEEE DOI
2004
convolutional neural nets, face recognition, feature extraction,
hidden feature removal, image classification,
Robustness
BibRef
Balakrishnan, G.,
Dalca, A.,
Zhao, A.,
Guttag, J.,
Durand, F.,
Freeman, W.,
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions,
ICCV19(171-180)
IEEE DOI
2004
convolutional neural nets, face recognition, gait analysis,
image classification, image motion analysis, image restoration,
BibRef
Garg, S.,
Ramakrishnan, G.,
Thumbe, V.,
Interpretable Inference Graphs for Face Recognition,
IVCNZ19(1-6)
IEEE DOI
2004
convolutional neural nets, face recognition, feature extraction,
graph theory, image classification, image colour analysis, Face Recognition
BibRef
Wang, X.,
Wang, S.,
Shi, H.,
Wang, J.,
Mei, T.,
Co-Mining: Deep Face Recognition With Noisy Labels,
ICCV19(9357-9366)
IEEE DOI
2004
convolutional neural nets, data mining, face recognition,
learning (artificial intelligence), loss values, noisy labels,
Robustness
BibRef
Chai, Z.,
Li, S.,
Meng, H.,
Lai, S.,
Wei, X.,
Zhang, J.,
A Progressive Learning Framework for Unconstrained Face Recognition,
LFR19(2703-2710)
IEEE DOI
2004
face recognition, learning (artificial intelligence),
light weight backbone architecture, progressive learning,
BibRef
Cheng, Y.,
Li, Y.,
Liu, Q.,
Yao, Y.,
Pedapudi, V.S.V.K.,
Fan, X.,
Su, C.,
Shen, S.,
A Graph Based Unsupervised Feature Aggregation for Face Recognition,
LFR19(2711-2720)
IEEE DOI
2004
directed graphs, face recognition, feature extraction,
Gaussian distribution, graph theory, iterative methods,
deep learning
BibRef
Martindez-Díaz, Y.,
Luevano, L.S.,
Mendez-Vazquez, H.,
Nicolas-Diaz, M.,
Chang, L.,
Gonzalez-Mendoza, M.,
ShuffleFaceNet: A Lightweight Face Architecture for Efficient and
Highly-Accurate Face Recognition,
LFR19(2721-2728)
IEEE DOI
2004
convolutional neural nets, face recognition,
neural net architecture, parametric rectified linear unit,
efficient architectures
BibRef
Lyu, Y.,
Jiang, J.,
Zhang, K.,
Hua, Y.,
Cheng, M.,
Factorizing and Reconstituting Large-Kernel MBConv for Lightweight
Face Recognition,
LFR19(2689-2697)
IEEE DOI
2004
face recognition, feature extraction, image reconstruction,
neural nets, neural architecture search, NAS,
Nueral Architecture Search
BibRef
Li, X.,
Wang, F.,
Hu, Q.,
Leng, C.,
AirFace: Lightweight and Efficient Model for Face Recognition,
LFR19(2678-2682)
IEEE DOI
2004
computer vision, convolutional neural nets, face recognition,
learning (artificial intelligence), neural net architecture,
loss function
BibRef
Yu, H.,
Fan, Y.,
Chen, K.,
Yan, H.,
Lu, X.,
Liu, J.,
Xie, D.,
Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face
Recognition,
LFR19(2662-2669)
IEEE DOI
2004
data handling, face recognition, image classification,
image representation, labeled training dataset, known identities,
Deep Convolutional Networks
BibRef
Smirnov, E.[Evgeny],
Oleinik, A.[Andrei],
Lavrentev, A.[Aleksandr],
Shulga, E.[Elizaveta],
Galyuk, V.[Vasiliy],
Garaev, N.[Nikita],
Zakuanova, M.[Margarita],
Melnikov, A.[Aleksandr],
Face Representation Learning using Composite Mini-Batches,
DFW19(551-559)
IEEE DOI
2004
face recognition, image representation, image sampling,
interpolation, learning (artificial intelligence),
Computer Vision
BibRef
Awiszus, M.,
Ackermann, H.,
Rosenhahn, B.,
Learning Disentangled Representations via Independent Subspaces,
RSL-CV19(560-568)
IEEE DOI
2004
face recognition, image colour analysis, image segmentation,
learning (artificial intelligence), neural nets,
Latent Space Editing
BibRef
Zee, T.,
Gali, G.,
Nwogu, I.,
Enhancing Human Face Recognition with an Interpretable Neural Network,
DFW19(514-522)
IEEE DOI
2004
convolutional neural nets, face recognition, feature extraction,
similar-looking actresses, recognition task,
Siamese networks
BibRef
Chen, K.[Ken],
Wu, Y.C.[Yi-Chao],
Qin, H.[Haoyu],
Liang, D.[Ding],
Liu, X.B.[Xue-Bo],
Yan, J.J.[Jun-Jie],
R3 Adversarial Network for Cross Model Face Recognition,
CVPR19(9860-9868).
IEEE DOI
2002
BibRef
Zhao, K.[Kai],
Xu, J.Y.[Jing-Yi],
Cheng, M.M.[Ming-Ming],
RegularFace: Deep Face Recognition via Exclusive Regularization,
CVPR19(1136-1144).
IEEE DOI
2002
BibRef
Hu, W.[Wei],
Huang, Y.Y.[Yang-Yu],
Zhang, F.[Fan],
Li, R.R.[Rui-Rui],
Noise-Tolerant Paradigm for Training Face Recognition CNNs,
CVPR19(11879-11888).
IEEE DOI
2002
BibRef
Zhong, Y.Y.[Yao-Yao],
Deng, W.H.[Wei-Hong],
Wang, M.[Mei],
Hu, J.[Jiani],
Peng, J.T.[Jian-Teng],
Tao, X.Q.[Xun-Qiang],
Huang, Y.H.[Yao-Hai],
Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data,
CVPR19(7804-7813).
IEEE DOI
2002
BibRef
Deng, J.K.[Jian-Kang],
Guo, J.[Jia],
Xue, N.[Niannan],
Zafeiriou, S.P.[Stefanos P.],
ArcFace: Additive Angular Margin Loss for Deep Face Recognition,
CVPR19(4685-4694).
IEEE DOI
2002
BibRef
Wang, Z.D.[Zhong-Dao],
Zheng, L.[Liang],
Li, Y.[Yali],
Wang, S.J.[Sheng-Jin],
Linkage Based Face Clustering via Graph Convolution Network,
CVPR19(1117-1125).
IEEE DOI
2002
BibRef
Cirne, M.,
Andaló, F.,
Dias, R.,
Resek, T.,
Bertocco, G.,
Torres, R.d.S.,
Rocha, A.,
Deep Face Verification for Spherical Images,
ICIP19(3292-3296)
IEEE DOI
1910
Face verification, spherical images,
convolutional neural networks, equirectangular projection
BibRef
Wei, X.,
Wang, H.,
Scotney, B.,
Wan, H.,
Gicoface: Global Information-Based Cosine Optimal Loss for Deep Face
Recognition,
ICIP19(3457-3461)
IEEE DOI
1910
Face recognition, Deep learning, CNNs, Loss function, Discriminative ability
BibRef
Meng, X.,
Yan, Y.,
Chen, S.,
Wang, H.,
A Cascaded Noise-Robust Deep CNN for Face Recognition,
ICIP19(3487-3491)
IEEE DOI
1910
face recognition, image denoising, deep CNN, dense connectivity
BibRef
Ardakani, P.B.[Parichehr B.],
Velazquez, D.[Diego],
Gonfaus, J.M.[Josep M.],
Rodríguez, P.[Pau],
Roca, F.X.[F. Xavier],
Gonzàlez, J.[Jordi],
Catastrophic Interference in Disguised Face Recognition,
IbPRIA19(II:64-75).
Springer DOI
1910
Neural networks to completely and abruptly forget previously known
information when learning new information.
BibRef
Xu, T.,
Garrod, O.,
Scholte, S.H.,
Ince, R.,
Schyns, P.G.,
Using Psychophysical Methods to Understand Mechanisms of Face
Identification in a Deep Neural Network,
Cognitive18(2057-20578)
IEEE DOI
1812
Face, Training, Lighting, Testing, Visualization,
BibRef
Wang, H.[Hao],
Wang, Y.T.[Yi-Tong],
Zhou, Z.[Zheng],
Ji, X.[Xing],
Gong, D.H.[Di-Hong],
Zhou, J.C.[Jing-Chao],
Li, Z.F.[Zhi-Feng],
Liu, W.[Wei],
CosFace: Large Margin Cosine Loss for Deep Face Recognition,
CVPR18(5265-5274)
IEEE DOI
1812
Face, Face recognition, Testing, Mars, Training, Feature extraction,
Task analysis
BibRef
Savchenko, A.,
Efficient Statistical Face Recognition Using Trigonometric Series and
CNN Features,
ICPR18(3262-3267)
IEEE DOI
1812
Face recognition, Training, Complexity theory, Kernel,
Feature extraction, Task analysis, Estimation
BibRef
Kang, B.N.[Bong-Nam],
Kim, Y.[Yonghyun],
Kim, D.J.[Dai-Jin],
Pairwise Relational Networks for Face Recognition,
ECCV18(II: 646-663).
Springer DOI
1810
BibRef
Wang, Y.T.[Yi-Tong],
Gong, D.H.[Di-Hong],
Zhou, Z.[Zheng],
Ji, X.[Xing],
Wang, H.[Hao],
Li, Z.F.[Zhi-Feng],
Liu, W.[Wei],
Zhang, T.[Tong],
Orthogonal Deep Features Decomposition for Age-Invariant Face
Recognition,
ECCV18(XV: 764-779).
Springer DOI
1810
BibRef
Rao, Q.,
Yu, B.,
Yang, Y.,
Feng, B.,
Knot Magnify Loss for Face Recognition,
ICIP18(2396-2400)
IEEE DOI
1809
Face, Training, Face recognition, Protocols, Feature extraction,
Task analysis, Measurement, Deep convolutional neural networks,
Quality imbalance
BibRef
Barbosa Kloss, R.,
Jordao, A.,
Schwartz, W.R.,
Face Verification: Strategies for Employing Deep Models,
FG18(258-262)
IEEE DOI
1806
Computational modeling, Face, Feature extraction, Machine learning,
Measurement, Standards, Task analysis, Artificial Neural Networks,
Transfer learning
BibRef
Qian, Y.,
Deng, W.,
Hu, J.,
Task Specific Networks for Identity and Face Variation,
FG18(271-277)
IEEE DOI
1806
Databases, Face, Face recognition, Feature extraction,
Image reconstruction, Lighting, Task analysis, face recognition,
task specific
BibRef
Guo, G.,
Zhang, N.,
What Is the Challenge for Deep Learning in Unconstrained Face
Recognition?,
FG18(436-442)
IEEE DOI
1806
Databases, Face, Face recognition, Image quality, Machine learning,
Probes, Protocols, Deep Learning, Face recognition, challenge,
unconstrained face recognition
BibRef
Luo, Z.,
Hu, J.,
Deng, W.,
Shen, H.,
Deep Unsupervised Domain Adaptation for Face Recognition,
FG18(453-457)
IEEE DOI
1806
Databases, Face, Face recognition, Neural networks, Task analysis,
Training, Training data, face recognition,
unsupervised domain adaptation
BibRef
Iqbal, A.,
Seghouane, A.K.,
An Approach for Sequential Dictionary Learning in Nonuniform Noise,
DICTA17(1-5)
IEEE DOI
1804
approximation theory, data analysis, face recognition,
image coding, image denoising, image representation,
Sparse matrices
See also Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.
BibRef
Su, C.,
Yan, Y.,
Chen, S.,
Wang, H.,
An efficient deep neural networks training framework for robust face
recognition,
ICIP17(3800-3804)
IEEE DOI
1803
Complexity theory, Computer architecture, Convergence, Face,
Face recognition, Neural networks, Training, Face recognition,
triplet loss function
BibRef
López-Avila, L.[Leyanis],
Plasencia-Calaña, Y.[Yenisel],
Martínez-Díaz, Y.[Yoanna],
Méndez-Vázquez, H.[Heydi],
On the Use of Pre-trained Neural Networks for Different Face
Recognition Tasks,
CIARP17(356-364).
Springer DOI
1802
BibRef
Neto, J.B.C.[João Baptista Cardia],
Marana, A.N.[Aparecido Nilceu],
Utilizing Deep Learning and 3DLBP for 3D Face Recognition,
CIARP17(135-142).
Springer DOI
1802
BibRef
Gecer, B.,
Balntas, V.,
Kim, T.K.,
Learning Deep Convolutional Embeddings for Face Representation Using
Joint Sample- and Set-Based Supervision,
AMFG17(1665-1672)
IEEE DOI
1802
Face, Face recognition, Magnetic losses, Support vector machines, Training
BibRef
Hasnat, A.,
Bohné, J.,
Milgram, J.,
Gentric, S.,
Chen, L.,
DeepVisage: Making Face Recognition Simple Yet With Powerful
Generalization Skills,
AMFG17(1682-1691)
IEEE DOI
1802
Computational modeling, Convolution, Face, Face recognition,
Training, Training data
BibRef
Manmatha, R.,
Wu, C.Y.,
Smola, A.J.[Alexander J.],
Krähenbühl, P.[Philipp],
Sampling Matters in Deep Embedding Learning,
ICCV17(2859-2867)
IEEE DOI
1802
face recognition, image retrieval, image sampling,
learning (artificial intelligence), neural nets,
Training
BibRef
Ming, Z.,
Chazalon, J.,
Luqman, M.M.,
Visani, M.,
Burie, J.C.,
Simple Triplet Loss Based on Intra/Inter-Class Metric Learning for
Face Verification,
AMFG17(1656-1664)
IEEE DOI
1802
Benchmark testing, Face, Face recognition, Feature extraction,
Measurement, Training, Visualization
BibRef
Han, B.B.[Bing-Bing],
Zhang, Z.H.[Zhi-Hong],
Xu, C.Y.[Chuan-Yu],
Wang, B.Z.[Bei-Zhan],
Hu, G.S.[Guo-Sheng],
Bai, L.[Lu],
Hong, Q.Q.[Qing-Qi],
Hancock, E.R.[Edwin R.],
Deep Face Model Compression Using Entropy-Based Filter Selection,
CIAP17(II:127-136).
Springer DOI
1711
BibRef
Shen, H.,
Han, S.,
Philipose, M.,
Krishnamurthy, A.,
Fast Video Classification via Adaptive Cascading of Deep Models,
CVPR17(2197-2205)
IEEE DOI
1711
Adaptation models, Cameras, Face recognition, Motion pictures,
Neural networks, Training
BibRef
Li, Y.,
Lin, G.,
Zhuang, B.,
Liu, L.,
Shen, C.,
van den Hengel, A.J.[Anton J.],
Sequential Person Recognition in Photo Albums with a Recurrent
Network,
CVPR17(5660-5668)
IEEE DOI
1711
Context modeling, Face recognition, Predictive models, Training, Visualization
BibRef
Kang, B.N.,
Kim, Y.,
Kim, D.,
Deep Convolutional Neural Network Using Triplets of Faces, Deep
Ensemble, and Score-Level Fusion for Face Recognition,
Biometrics17(611-618)
IEEE DOI
1709
Bayes methods, Convolution, Databases, Feature extraction,
IP networks, Image resolution, Training
BibRef
Tripathi, S.,
Dane, G.,
Kang, B.,
Bhaskaran, V.,
Nguyen, T.,
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object
Detection in Embedded Systems,
ECVW17(411-420)
IEEE DOI
1709
Detectors, Face detection, Mathematical model, Object detection,
Quantization (signal), Real-time systems, Training
BibRef
Nakada, M.,
Wang, H.,
Terzopoulos, D.,
AcFR: Active Face Recognition Using Convolutional Neural Networks,
Cognition17(35-40)
IEEE DOI
1709
Computational modeling, 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
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FG17(941-946)
IEEE DOI
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Face, Feature extraction, Neural networks, Probabilistic logic,
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FG17(673-680)
IEEE DOI
1707
Face, Face recognition, Feature extraction, Media, Metadata,
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Robust FEC-CNN: A High Accuracy Facial Landmark Detection System,
FaceWild17(2044-2050)
IEEE DOI
1709
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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
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Peng, X.,
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ICPR16(1442-1447)
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1705
Face, Face recognition, Feature extraction, Image recognition,
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VCIP16(1-4)
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Databases. For criminal id.
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1611
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view-invariant representation,
ICIP16(3244-3248)
IEEE DOI
1610
Brain modeling
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Grundström, J.[Jakob],
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Sudowe, P.,
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ChaLearnDec15(329-337)
IEEE DOI
1602
Benchmark testing
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Hu, G.,
Yang, Y.,
Yi, D.,
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ChaLearnDec15(384-392)
IEEE DOI
1602
Convolutional codes
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Dam, N.[Nhan],
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computer vision
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ICIP14(338-342)
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evolutionary computation
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Face recognition using a digital neural network with self-organising
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Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Face Analysis, Profiles .