Spatio-temporal convolutional features with nested LSTM for facial expression recognition

Neurocomputing - Tập 317 - Trang 50-57 - 2018
Zhenbo Yu1, Guangcan Liu1, Qingshan Liu1, Jiankang Deng2
1Jiangsu Key Laboratory of Big Data Analysis Technology, School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China
2Department of Computing, Imperial College London, UK Office: 351 Huxley Building, 180 Queens Gate, SW7 2AZ, UK

Tài liệu tham khảo

Ekman, 1971, Constants across cultures in the face and emotion, J. Pers. Soc. Psychol., 17, 124, 10.1037/h0030377 Li, 2006, Expression-invariant face recognition with expression classification, 77 Deng, 2017, Marginal loss for deep face recognition, 4 Deng, 2018, Additive angular margin loss for deep face recognition, CoRR Deng, 2018, UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition Deng, 2016, M3 csr: Multi-view, multi-scale and multi-component cascade shape regression, Image Vision Comput., 47, 19, 10.1016/j.imavis.2015.11.005 Yang, 2015, Facial shape tracking via spatio-temporal cascade shape regression, 41 Liu, 2017, Robust facial landmark tracking via cascade regression, Pattern Recognit., 66, 53, 10.1016/j.patcog.2016.12.024 Deng, 2015, Low rank driven robust facial landmark regression, Neurocomputing, 151, 196, 10.1016/j.neucom.2014.09.052 Liu, 2016, Dual sparse constrained cascade regression for robust face alignment, IEEE Trans. Image Process., 25, 700, 10.1109/TIP.2015.2502485 Liu, 2017, Adaptive cascade regression model for robust face alignment, IEEE Trans. Image Process., 26, 797, 10.1109/TIP.2016.2633939 Zafeiriou, 2017, The menpo facial landmark localisation challenge: a step towards the solution Deng, 2018, Cascade multi-view hourglass model for robust 3d face alignment, 399 Deng, 2017, Joint multi-view face alignment in the wild, CoRR Saudagare, 2012, Facial expression recognition using neural network can overview, Int. J. Soft Comput. Eng., 2, 238 Jung, 2015, Joint fine-tuning in deep neural networks for facial expression recognition, 2983 Liu, 2014, Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition, 1749 Liu, 2014, Facial expression recognition via a boosted deep belief network, 1805 M. Valstar, M. Pantic, Induced disgust, happiness and surprise: An addition to the mmi facial expression database, Workshop on Emotion Corpora for Research on Emotion & Affect (2010) 65–70. Metaxas, 2012, Learning active facial patches for expression analysis, 2562 Sikka, 2012, Exploring bag of words architectures in the facial expression domain, 250 Zhong, 2012, Learning multiscale active facial patches for expression analysis., 2562 Yann, 1990, Handwritten digit recognition with a back-propagation network, 396 Han, 2016, Incremental boosting convolutional neural network for facial action unit recognition, 109 Jaiswal, 2016, Deep learning the dynamic appearance and shape of facial action units, 1 Zhao, 2016, Peak-piloted deep network for facial expression recognition, 425 Ebrahimi Kahou, 2015, Recurrent neural networks for emotion recognition in video, 467 Liu, 2016, Video-based emotion recognition using CNN-RNN and c3d hybrid networks, 445 Yang, 2010, Exploring facial expressions with compositional features, 2638 Graves, 2009, A novel connectionist system for unconstrained handwriting recognition, IEEE Trans. Pattern Anal. Mach. Intell., 31, 855, 10.1109/TPAMI.2008.137 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Wang, 2015, Visual classification by ℓ1 -hypergraph modeling, IEEE Trans. Knowl. Data Eng., 27, 2564, 10.1109/TKDE.2015.2415497 Wang, 2016, Beyond object proposals: Random crop pooling for multi-label image recognition, IEEE Trans. Image Process., 25, 5678, 10.1109/TIP.2016.2612829 Dan Guo, 2018, Hierarchical lstm for sign language translation Du, 2016, Learning spatiotemporal features with 3d convolutional networks, 4489 Zhang, 2017, 3120 Zhu, 2017, Multimodal gesture recognition using 3-d convolution and convolutional lstm, IEEE Access, 5, 4517, 10.1109/ACCESS.2017.2684186 Lucey, 2010, The extended Cohn-Kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression, 94 Zhao, 2011, Facial expression recognition from near-infrared videos, Image Vision Comput., 29, 607, 10.1016/j.imavis.2011.07.002 Valstar, 2016, Fera 2015 - second facial expression recognition and analysis challenge, 1 Bargal, 2016, Emotion recognition in the wild from videos using images, 433 Dhall, 2016, Emotiw 2016: video and group-level emotion recognition challenges, 427 Sikka, 2016, Lomo: latent ordinal model for facial analysis in videos, 5580 Yu, 2015, Image based static facial expression recognition with multiple deep network learning, 435 Kim, 2015, Hierarchical committee of deep CNNs with exponentially-weighted decision fusion for static facial expression recognition, 427 Simonyan, 2014, Very deep convolutional networks for large-scale image recognition He, 2016, Deep residual learning for image recognition, 770 Yao, 2016, Holonet: towards robust emotion recognition in the wild, 472 Liu, 2014, 143 He, 2014, Spatial pyramid pooling in deep convolutional networks for visual recognition, 346 Liu, 2014, Combining multiple kernel methods on riemannian manifold for emotion recognition in the wild, 494 Zhang, 2016, Joint face detection and alignment using multitask cascaded convolutional networks, IEEE Signal Process. Lett., 23, 1499, 10.1109/LSP.2016.2603342 Scovanner, 2007, A 3-dimensional sift descriptor and its application to action recognition, 357 Ofodile, 2017, Automatic recognition of deceptive facial expressions of emotion H. Ding, S.K. Zhou, R. Chellappa, Facenet2expnet: regularizing a deep face recognition net for expression recognition (2017) 118–126. Afshar, 2016, Facial expression recognition in the wild using improved dense trajectories and fisher vector encoding, 1517 Yu, 2017, Deeper cascaded peak-piloted network for weak expression recognition, Visual Comput., 6, 1 Elaiwat, 2015, A spatio-temporal RBM-based model for facial expression recognition, Pattern Recognit., 49, 152 Huang, 2017, Densely connected convolutional networks, 4, 2261 Abadi, 2016, Tensorflow: large-scale machine learning on heterogeneous distributed systems Glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, J. Mach. Learn. Res., 9, 249 Yce, 2015, Discriminant multi-label manifold embedding for facial action unit detection, 1