21.3.6.1.1 Face Expression Recognition Using Learning, Neural Nets

Chapter Contents (Back)
Faces, Expression. Facial Expressions. Expressions. Learning. Neural Nets.

Suzuki, K.[Kenji], Yamada, H.[Hiroshi], Hashimoto, S.[Shuji],
A similarity-based neural network for facial expression analysis,
PRL(28), No. 9, 1 July 2007, pp. 1104-1111.
Elsevier DOI 0704
Similarity-based neural network; Multidimensional perceptual scaling; Facial expressions; Stable dynamic parameter adaptation; Nonlinear mapping BibRef

Dornaika, F., Lazkano, E., Sierra, B.,
Improving dynamic facial expression recognition with feature subset selection,
PRL(32), No. 5, 1 April 2011, pp. 740-748.
Elsevier DOI 1103
Dynamic facial expression recognition; Feature subset selection; Estimation of Distribution Algorithms; Machine learning approaches; Wrapper technique BibRef

Lopes, A.T.[André Teixeira], de Aguiar, E.[Edilson], de Souza, A.F.[Alberto F.], Oliveira-Santos, T.[Thiago],
Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order,
PR(61), No. 1, 2017, pp. 610-628.
Elsevier DOI 1609
Facial expression recognition BibRef

Bougourzi, F.[Fares], Mokrani, K.[Karim], Ruichek, Y.[Yassine], Dornaika, F.[Fadi], Ouafi, A.[Abdelkrim], Taleb-Ahmed, A.[Abdelmalik],
Fusion of transformed shallow features for facial expression recognition,
IET-IPR(13), No. 9, 18 July 2019, pp. 1479-1489.
DOI Link 1907
BibRef

Yan, H., Ang, M.H., Poo, A.N.,
Adaptive discriminative metric learning for facial expression recognition,
IET-Bio(1), No. 3, September 2012, pp. 160-167.
DOI Link 1305
BibRef

Liao, C.T.[Chia-Te], Chuang, H.J.[Hui-Ju], Duan, C.H.[Chih-Hsueh], Lai, S.H.[Shang-Hong],
Learning spatial weighting for facial expression analysis via constrained quadratic programming,
PR(46), No. 11, November 2013, pp. 3103-3116.
Elsevier DOI 1306
BibRef
Earlier:
Learning spatial weighting via quadratic programming for facial expression analysis,
CVPR4HB10(86-93).
IEEE DOI 1006
Facial expression analysis; Quadratic programming; Expression recognition; Expression intensity estimation BibRef

Zhang, W.[Wei], Zhang, Y.[Youmei], Ma, L.[Lin], Guan, J.W.[Jing-Wei], Gong, S.J.[Shi-Jie],
Multimodal learning for facial expression recognition,
PR(48), No. 10, 2015, pp. 3191-3202.
Elsevier DOI 1507
Multimodal learning BibRef

Wang, L.[Li], Wang, K.[Ke], Li, R.F.[Rui-Feng],
Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition,
IET-CV(9), No. 5, 2015, pp. 655-662.
DOI Link 1511
emotion recognition BibRef

Zen, G., Porzi, L., Sangineto, E., Ricci, E., Sebe, N.,
Learning Personalized Models for Facial Expression Analysis and Gesture Recognition,
MultMed(18), No. 4, April 2016, pp. 775-788.
IEEE DOI 1604
Data models BibRef

Ren, F., Huang, Z.,
Automatic Facial Expression Learning Method Based on Humanoid Robot XIN-REN,
HMS(46), No. 6, December 2016, pp. 810-821.
IEEE DOI 1612
control engineering computing BibRef

Sun, Z.[Zhe], Hu, Z.P.[Zheng-Ping], Wang, M.[Meng], Zhao, S.H.[Shu-Huan],
Individual-free representation-based classification for facial expression recognition,
SIViP(11), No. 4, May 2017, pp. 597-604.
WWW Link. 1704
BibRef

Sun, Z.[Zhe], Hu, Z.P.[Zheng-Ping], Wang, M.[Meng], Zhao, S.H.[Shu-Huan],
Discriminative feature learning-based pixel difference representation for facial expression recognition,
IET-CV(11), No. 8, December 2017, pp. 675-682.
DOI Link 1712
BibRef

Sun, Z.[Zhe], Hu, Z.P.[Zheng-Ping], Chiong, R.[Raymond], Wang, M.[Meng], Zhao, S.H.[Shu-Huan],
An adaptive weighted fusion model with two subspaces for facial expression recognition,
SIViP(12), No. 5, July 2018, pp. 835-843.
Springer DOI 1806
BibRef

Liu, Y.Y.[Yuan-Yuan], Yuan, X.H.[Xiao-Hui], Gong, X.[Xi], Xie, Z.[Zhong], Fang, F.[Fang], Luo, Z.W.[Zhong-Wen],
Conditional convolution neural network enhanced random forest for facial expression recognition,
PR(84), 2018, pp. 251-261.
Elsevier DOI 1809
Classification, Feature extraction, Facial expression recognition, Head pose alignment, Conditional CoNERF BibRef

Li, T.H.[Tai-Hao], Du, C.F.[Cui-Fen], Naren, T.[Tuya], Chen, Z.Q.[Zhi-Qiang], Liu, S.P.[Shu-Peng], Zhou, J.S.[Jian-She], Xu, X.Y.[Xiao-Yin],
Using feature points and angles between them to recognise facial expression by a neural network approach,
IET-IPR(12), No. 11, November 2018, pp. 1951-1955.
DOI Link 1810
BibRef

Yu, Z.B.[Zhen-Bo], Liu, Q.S.[Qin-Shan], Liu, G.C.[Guang-Can],
Deeper cascaded peak-piloted network for weak expression recognition,
VC(34), No. 12, December 2018, pp. 1691-1699.
WWW Link. 1811
BibRef

Yan, H.B.[Hai-Bin],
Collaborative discriminative multi-metric learning for facial expression recognition in video,
PR(75), No. 1, 2018, pp. 33-40.
Elsevier DOI 1712
Facial expression recognition BibRef

Gupta, O.[Otkrist], Raviv, D.[Dan], Raskar, R.[Ramesh],
Illumination invariants in deep video expression recognition,
PR(76), No. 1, 2018, pp. 25-35.
Elsevier DOI 1801
Deep learning BibRef

Salmam, F.Z.[Fatima Zahra], Madani, A.[Abdellah], Kissi, M.[Mohamed],
Fusing multi-stream deep neural networks for facial expression recognition,
SIViP(13), No. 3, April 2019, pp. 609-616.
WWW Link. 1904
BibRef

Xie, S.Y.[Si-Yue], Hu, H.F.[Hai-Feng], Wu, Y.[Yongbo],
Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition,
PR(92), 2019, pp. 177-191.
Elsevier DOI 1905
Attention, Convolutional neural network, Facial expression recognition, Salient expressional region descriptor BibRef

Gan, Y.L.[Yan-Ling], Chen, J.Y.[Jing-Ying], Xu, L.[Luhui],
Facial expression recognition boosted by soft label with a diverse ensemble,
PRL(125), 2019, pp. 105-112.
Elsevier DOI 1909
Facial expression recognition, Convolutional neural network, Soft label, Label-level perturbation strategy, Ensemble classifier BibRef

Gupta, O.[Otkrist], Raviv, D.[Dan], Raskar, R.[Ramesh],
Multi-Velocity Neural Networks for Facial Expression Recognition in Videos,
AffCom(10), No. 2, April 2019, pp. 290-296.
IEEE DOI 1906
Videos, Training, Splines (mathematics), Face recognition, Convolutional codes, Biological neural networks, Deep learning, machine learning BibRef

Majumder, A.[Anima], Behera, L.[Laxmidhar], Subramanian, V.K.,
Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion,
Cyber(48), No. 1, January 2018, pp. 103-114.
IEEE DOI 1801
BibRef
Earlier: A1, A2, Only:
Facial Expression Recognition with Regional Features Using Local Binary Patterns,
CAIP13(556-563).
Springer DOI 1308
Active appearance model, Computer architecture, Data integration, Face, Face recognition, Feature extraction, support vector machine (SVM) BibRef

Zheng, W.M.[Wen-Ming], Zong, Y., Zhou, X.Y.[Xiao-Yan], Xin, M.,
Cross-Domain Color Facial Expression Recognition Using Transductive Transfer Subspace Learning,
AffCom(9), No. 1, January 2018, pp. 21-37.
IEEE DOI 1804
face recognition, feature extraction, image classification, image colour analysis, learning (artificial intelligence), transductive transfer learning BibRef

Zheng, W.M.[Wen-Ming], Zhou, X.Y.[Xiao-Yan],
Cross-pose color facial expression recognition using transductive transfer linear discriminat analysis,
ICIP15(1935-1939)
IEEE DOI 1512
Color facial expression recognition BibRef

Xie, S.Y.[Si-Yue], Hu, H.F.[Hai-Feng], Yin, Z.[Ziyu],
Facial expression recognition using intra-class variation reduced features and manifold regularisation dictionary pair learning,
IET-CV(12), No. 4, June 2018, pp. 458-465.
DOI Link 1805
BibRef

Du, L.S.[Ling-Shuang], Hu, H.F.[Hai-Feng],
Weighted Patch-based Manifold Regularization Dictionary Pair Learning model for facial expression recognition using Iterative Optimization Classification Strategy,
CVIU(186), 2019, pp. 13-24.
Elsevier DOI 1908
Facial Expression Recognition, Weighted Patch-based LBP, Manifold Regularization Dictionary Pair Learning model, Iterative Optimization Classification Strategy BibRef

Huang, Y.[Ying], Yan, Y.[Yan], Chen, S.[Si], Wang, H.Z.[Han-Zi],
Expression-targeted feature learning for effective facial expression recognition,
JVCIR(55), 2018, pp. 677-687.
Elsevier DOI 1809
Facial expression recognition, Multi-task learning, Feature learning, Convolutional neural network BibRef

Zhang, Z.Z.[Zi-Zhao], Yan, Y.[Yan], Wang, H.Z.[Han-Zi],
Discriminative filter based regression learning for facial expression recognition,
ICIP13(1192-1196)
IEEE DOI 1402
Cost function BibRef

Li, S., Deng, W.,
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition,
IP(28), No. 1, January 2019, pp. 356-370.
IEEE DOI 1810
convolution, emotion recognition, expectation-maximisation algorithm, face recognition, deep learning BibRef

Li, S., Deng, W., Du, J.,
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild,
CVPR17(2584-2593)
IEEE DOI 1711
Compounds, Crowdsourcing, Databases, Face recognition, Gold, Machine learning, Reliability BibRef

Dai, S., Man, H.,
Mixture Statistic Metric Learning for Robust Human Action and Expression Recognition,
CirSysVideo(28), No. 10, October 2018, pp. 2484-2499.
IEEE DOI 1811
BibRef
Earlier:
A statistic manifold kernel with graph embedding discriminant analysis for action and expression recognition,
ICIP17(1792-1796)
IEEE DOI 1803
Measurement, Kernel, Manifolds, Face recognition, Visualization, Indexes, Action recognition, facial expression recognition, mixture statistical metric learning. Task analysis, Visualization, YouTube, Statistic manifold kernel BibRef

Kuo, C., Lai, S., Sarkis, M.,
A Compact Deep Learning Model for Robust Facial Expression Recognition,
AMFG18(2202-22028)
IEEE DOI 1812
Databases, Training, Face recognition, Hidden Markov models, Face, Image recognition, Image sequences BibRef

Liu, X.F.[Xiao-Feng], Vijaya Kumar, B.V.K., Jia, P.[Ping], You, J.[Jane],
Hard negative generation for identity-disentangled facial expression recognition,
PR(88), 2019, pp. 1-12.
Elsevier DOI 1901
Hard negative generation, Adaptive metric learning, Face normalization, Facial expression recognition BibRef

Li, Y., Zeng, J., Shan, S., Chen, X.,
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism,
IP(28), No. 5, May 2019, pp. 2439-2450.
IEEE DOI 1903
BibRef
Earlier:
Patch-Gated CNN for Occlusion-aware Facial Expression Recognition,
ICPR18(2209-2214)
IEEE DOI 1812
convolutional neural nets, emotion recognition, face recognition, learning (artificial intelligence), gate unit. Task analysis, Feature extraction, Mouth, Computational modeling. BibRef

Kim, D.H.[Dae Hoe], Baddar, W.J.[Wissam J.], Jang, J., Ro, Y.M.[Yong Man],
Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition,
AffCom(10), No. 2, April 2019, pp. 223-236.
IEEE DOI 1906
BibRef
Earlier: A2, A1, A4, Only:
Learning Features Robust to Image Variations with Siamese Networks for Facial Expression Recognition,
MMMod17(I: 189-200).
Springer DOI 1701
Feature extraction, Face recognition, Robustness, Machine learning, Training, long short-term memory (LSTM) BibRef

Sadeghi, H.[Hamid], Raie, A.A.[Abolghasem A.],
Histogram distance metric learning for facial expression recognition,
JVCIR(62), 2019, pp. 152-165.
Elsevier DOI 1908
Metric learning, Local metric learning, Chi-squared distance, Histogram classification, Facial expression recognition in the wild BibRef

Ye, Y.S.[Ying-Sheng], Zhang, X.M.[Xing-Ming], Lin, Y.B.[Yu-Bei], Wang, H.X.[Hao-Xiang],
Facial expression recognition via region-based convolutional fusion network,
JVCIR(62), 2019, pp. 1-11.
Elsevier DOI 1908
Facial expression recognition, Emotion recognition, Convolution neural network BibRef

Xie, L., Zhao, J., Wei, H., Zhang, K., Pang, G.,
Online Kernel-Based Structured Output SVM for Early Expression Detection,
SPLetters(26), No. 9, September 2019, pp. 1305-1309.
IEEE DOI 1909
face recognition, feature extraction, learning (artificial intelligence), support vector machines, deep features BibRef

Xie, W.C.[Wei-Cheng], Jia, X.[Xi], Shen, L.L.[Lin-Lin], Yang, M.[Meng],
Sparse deep feature learning for facial expression recognition,
PR(96), 2019, pp. 106966.
Elsevier DOI 1909
Expression recognition, Feature sparseness, Deep metric learning, Fine tuning, Generalization capability BibRef

Zeng, G., Zhou, J., Jia, X.[Xi], Xie, W.C.[Wei-Cheng], Shen, L.L.[Lin-Lin],
Hand-Crafted Feature Guided Deep Learning for Facial Expression Recognition,
FG18(423-430)
IEEE DOI 1806
Convolution, Face, Feature extraction, Loss measurement, Optimization, Training, deep metric learning, hand crafted feature BibRef

Fan, X.J.[Xi-Jian], Tjahjadi, T.[Tardi],
Fusing dynamic deep learned features and handcrafted features for facial expression recognition,
JVCIR(65), 2019, pp. 102659.
Elsevier DOI 1912
Convolutional neural network, Facial expression recognition, Feature extraction BibRef

Happy, S.L., Dantcheva, A.[Antitza], Bremond, F.[Francois],
A Weakly Supervised learning technique for classifying facial expressions,
PRL(128), 2019, pp. 162-168.
Elsevier DOI 1912
Weakly supervised learning, Facial expression recognition, Label smoothing BibRef

Gogic, I.[Ivan], Manhart, M.[Martina], Pandžic, I.S.[Igor S.], Ahlberg, J.[Jörgen],
Fast facial expression recognition using local binary features and shallow neural networks,
VC(36), No. 1, January 2020, pp. 97-112.
WWW Link. 2001
BibRef

Mai, S., Xing, S., Hu, H.,
Locally Confined Modality Fusion Network With a Global Perspective for Multimodal Human Affective Computing,
MultMed(22), No. 1, January 2020, pp. 122-137.
IEEE DOI 2001
Multimodal human affective computing, locally confined cross-modality interaction, bidirectional multiconnected LSTM BibRef

An, F.P.[Feng-Ping], Liu, Z.W.[Zhi-Wen],
Facial expression recognition algorithm based on parameter adaptive initialization of CNN and LSTM,
VC(36), No. 3, March 2020, pp. 483-498.
WWW Link. 2002
BibRef

Liang, D.[Dandan], Liang, H.G.[Hua-Gang], Yu, Z.B.[Zhen-Bo], Zhang, Y.[Yipu],
Deep convolutional BiLSTM fusion network for facial expression recognition,
VC(36), No. 3, March 2020, pp. 499-508.
WWW Link. 2002
BibRef

Agrawal, A.[Abhinav], Mittal, N.[Namita],
Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy,
VC(36), No. 2, February 2020, pp. 405-412.
WWW Link. 2002
BibRef

Li, K.[Kuan], Jin, Y.[Yi], Akram, M.W.[Muhammad Waqar], Han, R.[Ruize], Chen, J.W.[Jiong-Wei],
Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy,
VC(36), No. 2, February 2020, pp. 391-404.
WWW Link. 2002
BibRef

Wang, K.[Kai], Peng, X.J.[Xiao-Jiang], Yang, J.F.[Jian-Fei], Meng, D.B.[De-Bin], Qiao, Y.[Yu],
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition,
IP(29), 2020, pp. 4057-4069.
IEEE DOI 2002
Facial expression recognition, occlusion-robust and pose-invariant, region attention network, deep convolutional neural networks BibRef

Du, L.S.[Ling-Shuang], Wu, Y.B.[Yong-Bo], Hu, H.F.[Hai-Feng], Wang, W.X.[Wei-Xuan],
Self-adaptive weighted synthesised local directional pattern integrating with sparse autoencoder for expression recognition based on improved multiple kernel learning strategy,
IET-CV(14), No. 3, April 2020, pp. 73-83.
DOI Link 2003
BibRef

Zhang, H.P.[He-Peng], Huang, B.[Bin], Tian, G.H.[Guo-Hui],
Facial expression recognition based on deep convolution long short-term memory networks of double-channel weighted mixture,
PRL(131), 2020, pp. 128-134.
Elsevier DOI 2004
Facial expression recognition, Computer applications, CNN, LSTM BibRef

Sun, Z.[Zhe], Chiong, R.[Raymond], Hu, Z.P.[Zheng-Ping], Li, S.F.[Shu-Fang],
Deep subspace learning for expression recognition driven by a two-phase representation classifier,
SIViP(14), No. 3, April 2020, pp. 437-444.
WWW Link. 2004
BibRef

Rajan, S.[Saranya], Chenniappan, P.[Poongodi], Devaraj, S.[Somasundaram], Madian, N.[Nirmala],
Novel deep learning model for facial expression recognition based on maximum boosted CNN and LSTM,
IET-IPR(14), No. 7, 29 May 2020, pp. 1373-1381.
DOI Link 2005
BibRef

Bozorgtabar, B.[Behzad], Mahapatra, D.[Dwarikanath], Thiran, J.P.[Jean-Philippe],
ExprADA: Adversarial domain adaptation for facial expression analysis,
PR(100), 2020, pp. 107111.
Elsevier DOI 2005
Visual domain adaptation, Facial expression recognition, Adversarial learning BibRef

Lee, J.R.H.[J. R. Hou], Wong, A.,
TimeConvNets: A Deep Time Windowed Convolution Neural Network Design for Real-time Video Facial Expression Recognition,
CRV20(9-16)
IEEE DOI 2006
convolution, temporal, emotion, expression, dataset BibRef


Verma, M., Kobori, H., Nakashima, Y., Takemura, N., Nagahara, H.,
Facial Expression Recognition with Skip-Connection to Leverage Low-Level Features,
ICIP19(51-55)
IEEE DOI 1910
Facial expression recognition, facial landmarks, convolutional neural network, low level features. BibRef

Bai, M., Xie, W., Shen, L.,
Disentangled Feature Based Adversarial Learning for Facial Expression Recognition,
ICIP19(31-35)
IEEE DOI 1910
Disentangled feature, adversarial learning, expression disentangling, residual expression BibRef

Chen, J., Konrad, J., Ishwar, P.,
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition,
PRIV18(1651-165109)
IEEE DOI 1812
Face, Face recognition, Privacy, Visualization, Image recognition, Generative adversarial networks BibRef

Yang, H., Ciftci, U., Yin, L.,
Facial Expression Recognition by De-expression Residue Learning,
CVPR18(2168-2177)
IEEE DOI 1812
Face, Generators, Face recognition, Databases, Training data, Training BibRef

Lin, F., Hong, R., Zhou, W., Li, H.,
Facial Expression Recognition with Data Augmentation and Compact Feature Learning,
ICIP18(1957-1961)
IEEE DOI 1809
Face, Training, Databases, Face recognition, Shape, Solid modeling, cluster loss BibRef

Shen, F., Liu, J., Wu, P.,
Double Complete D-LBP with Extreme Learning Machine Auto-Encoder and Cascade Forest for Facial Expression Analysis,
ICIP18(1947-1951)
IEEE DOI 1809
Databases, Forestry, Feature extraction, Histograms, Face recognition, Principal component analysis, Training, cascade forest BibRef

Cai, J., Meng, Z., Khan, A.S., Li, Z., O'Reilly, J., Tong, Y.,
Island Loss for Learning Discriminative Features in Facial Expression Recognition,
FG18(302-309)
IEEE DOI 1806
Backpropagation, Databases, Face recognition, Head, Lighting, Propagation losses, Training, Convolutional Neural Network, Island Loss BibRef

Aneja, D.[Deepali], Chaudhuri, B., Colburn, A.[Alex], Faigin, G.[Gary], Shapiro, L.G.[Linda G.], Mones, B.[Barbara],
Learning to Generate 3D Stylized Character Expressions from Humans,
WACV18(160-169)
IEEE DOI 1806
computer animation, face recognition, feedforward neural nets, learning (artificial intelligence), 3D animated character rig, BibRef

Aneja, D.[Deepali], Colburn, A.[Alex], Faigin, G.[Gary], Shapiro, L.G.[Linda G.], Mones, B.[Barbara],
Modeling Stylized Character Expressions via Deep Learning,
ACCV16(II: 136-153).
Springer DOI 1704
BibRef

Mikheeva, O., Ek, C.H., Kjellstroem, H.,
Perceptual Facial Expression Representation,
FG18(179-186)
IEEE DOI 1806
Computational modeling, Data models, Encoding, Face, Neural networks, Semantics, Standards, facial expressions, representation learning, variational auto encoder BibRef

Li, Z., Wu, S., Xiao, G.,
Facial Expression Recognition by Multi-Scale CNN with Regularized Center Loss,
ICPR18(3384-3389)
IEEE DOI 1812
computer vision, convolution, face recognition, feature extraction, feedforward neural nets, image classification, regularized center loss BibRef

Dong, J.Y.[Jia-Yu], Zheng, H.C.[Hui-Cheng], Lian, L.[Lina],
Dynamic Facial Expression Recognition Based on Convolutional Neural Networks with Dense Connections,
ICPR18(3433-3438)
IEEE DOI 1812
Databases, Image sequences, Face, Training, Face recognition, Training data, Convolutional neural networks BibRef

Tran, E., Mayhew, M.B., Kim, H., Karande, P., Kaplan, A.D.,
Facial Expression Recognition Using a Large Out-of-Context Dataset,
Assist18(52-59)
IEEE DOI 1806
emotion recognition, face recognition, neural nets, FER+ dataset, MS-Celeb-1M dataset, emotion labels, emotion recognition model, Training BibRef

Gu, J., Hu, H., Xie, S.,
Enhanced dictionary pair learning sparse representation model for facial expression classification,
ICIP17(4467-4471)
IEEE DOI 1803
Dictionaries, Face, Feature extraction, Indexes, Training, dictionary pair learning, facial expression recognition, sparse representation BibRef

Mavani, V., Raman, S., Miyapuram, K.P.,
Facial Expression Recognition Using Visual Saliency and Deep Learning,
CogCV17(2783-2788)
IEEE DOI 1802
Face recognition, Feature extraction, Machine learning, Testing, Training, Visualization BibRef

Liu, X., Guo, Z., Li, S., Jia, P., Kong, L., You, J., Kumar, B.V.K.V.,
Permutation-Invariant Feature Restructuring for Correlation-Aware Image Set-Based Recognition,
ICCV19(4985-4995)
IEEE DOI 2004
face recognition, feature extraction, image sequences, learning (artificial intelligence), optimisation, Image reconstruction BibRef

Liu, X., Kumar, B.V.K.V.[B. V. K. V.], You, J., Jia, P.,
Adaptive Deep Metric Learning for Identity-Aware Facial Expression Recognition,
Biometrics17(522-531)
IEEE DOI 1709
Face recognition, Feature extraction, Measurement, Optimization, Training BibRef

Gui, L., Baltrušaitis, T., Morency, L.P.[Louis-Philippe],
Curriculum Learning for Facial Expression Recognition,
FG17(505-511)
IEEE DOI 1707
Complexity theory, Emotion recognition, Face, Face recognition, Machine learning, Neural networks, Training BibRef

Ding, H., Zhou, S.K., Chellappa, R.,
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition,
FG17(118-126)
IEEE DOI 1707
Convolution, Distribution functions, Face, Face recognition, Image recognition, Neurons, Training BibRef

Ghasemi, A., Baktashmotlagh, M., Denman, S., Sridharan, S., Tien, D.N., Fookes, C.,
Deep discovery of facial motions using a shallow embedding layer,
ICIP17(1567-1571)
IEEE DOI 1803
Feature extraction, Hidden Markov models, Kernel, Machine learning, Pain, Training BibRef

Ghasemi, A., Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
Discovery of facial motions using deep machine perception,
WACV16(1-7)
IEEE DOI 1606
Convolution BibRef

Huang, Y., Lu, H.Q.[Han-Qing],
Hybrid hypergraph construction for facial expression recognition,
ICPR16(4142-4147)
IEEE DOI 1705
Face, Face recognition, Image recognition, Mathematical model, Neural networks, Training BibRef

Sun, W.[Wenyun], Zhao, H.T.[Hai-Tao], Jin, Z.[Zhong],
3D Convolutional Neural Networks for Facial Expression Classification,
SFBA16(I: 528-543).
Springer DOI 1704
BibRef

Arbabzadah, F.[Farhad], Montavon, G.[Grégoire], Müller, K.R.[Klaus-Robert], Samek, W.[Wojciech],
Identifying Individual Facial Expressions by Deconstructing a Neural Network,
GCPR16(344-354).
Springer DOI 1611
BibRef

Karali, A.[Abubakrelsedik], Bassiouny, A.[Ahmad], El-Saban, M.[Motaz],
Facial expression recognition in the wild using rich deep features,
ICIP15(3442-3446)
IEEE DOI 1512
Facial expression recognition; deep neural networks features BibRef

Meng, Z.[Zibo], Liu, P.[Ping], Cai, J., Han, S.H.[Shiz-Hong], Tong, Y.[Yan],
Identity-Aware Convolutional Neural Network for Facial Expression Recognition,
FG17(558-565)
IEEE DOI 1707
Databases, Face recognition, Feature extraction, Image recognition, Measurement, Spatiotemporal phenomena, Training BibRef

Liu, P.[Ping], Han, S.H.[Shiz-Hong], Meng, Z.[Zibo], Tong, Y.[Yan],
Facial Expression Recognition via a Boosted Deep Belief Network,
CVPR14(1805-1812)
IEEE DOI 1409
BibRef

Liu, P.[Ping], Zhou, J.T.[Joey Tianyi], Tsang, I.W.H.[Ivor Wai-Hung], Meng, Z.[Zibo], Han, S.H.[Shiz-Hong], Tong, Y.[Yan],
Feature Disentangling Machine: A Novel Approach of Feature Selection and Disentangling in Facial Expression Analysis,
ECCV14(IV: 151-166).
Springer DOI 1408
BibRef

de Vries, G.J.[Gert-Jan], Pauws, S.[Steffen], Biehl, M.[Michael],
Facial Expression Recognition Using Learning Vector Quantization,
CAIP15(II:760-771).
Springer DOI 1511
BibRef

Li, W.[Wei], Li, M.[Min], Su, Z.[Zhong], Zhu, Z.G.[Zhi-Gang],
A deep-learning approach to facial expression recognition with candid images,
MVA15(279-282)
IEEE DOI 1507
Computational modeling BibRef

Jung, H.[Heechul], Lee, S.[Sihaeng], Yim, J., Park, S.[Sunjeong], Kim, J.[Junmo],
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition,
ICCV15(2983-2991)
IEEE DOI 1602
Databases BibRef

Jung, H.[Heechul], Lee, S.[Sihaeng], Park, S.[Sunjeong], Kim, B.J.[Byung-Ju], Kim, J.[Junmo], Lee, I.[Injae], Ahn, C.H.[Chung-Hyun],
Development of deep learning-based facial expression recognition system,
FCV15(1-4)
IEEE DOI 1506
Haar transforms BibRef

Fang, Y.C.[Yu-Chun], Chang, L.[Lu],
Multi-instance Feature Learning Based on Sparse Representation for Facial Expression Recognition,
MMMod15(I: 224-233).
Springer DOI 1501
BibRef

Liu, W.F.[Wei-Feng], Song, C.F.[Cai-Feng], Wang, Y.J.[Yan-Jiang],
Facial expression recognition based on discriminative dictionary learning,
ICPR12(1839-1842).
WWW Link. 1302
BibRef

Ptucha, R.[Raymond], Tsagkatakis, G.[Grigorios], Savakis, A.E.[Andreas E.],
Manifold based Sparse Representation for robust expression recognition without neutral subtraction,
BenchFace11(2136-2143).
IEEE DOI 1201
BibRef
And:
Manifold learning for simultaneous pose and facial expression recognition,
ICIP11(3021-3024).
IEEE DOI 1201
BibRef
Earlier: A1, A3, Only
Facial Expression Recognition Using Facial Features and Manifold Learning,
ISVC10(III: 301-309).
Springer DOI 1011
BibRef
And: A1, A3, Only
Pose estimation using facial feature points and manifold learning,
ICIP10(3261-3264).
IEEE DOI 1009
BibRef

Tax, D.M.J., Hendriks, E., Valstar, M.F.[Michel F.], Pantic, M.[Maja],
The Detection of Concept Frames Using Clustering Multi-instance Learning,
ICPR10(2917-2920).
IEEE DOI 1008
For facial expressions. Not model sequence, just concept (key) frames. BibRef

Isukapalli, R., Elgammal, A.M., Greiner, R.,
Learning to Identify Facial Expression During Detection Using Markov Decision Process,
FGR06(305-310).
IEEE DOI 0604
BibRef

Chen, X.[Xilen], Kwong, S.[Sam], Lu, Y.[Yan],
Human facial expression recognition based on learning subspace method,
ICME00(MP7). 0007
BibRef

Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Sptatio-Temporal Analysis for Face Expression Recognition .


Last update:Jul 10, 2020 at 16:03:35