Facial Expression Recognition Algorithm Based on Cosine Distance Loss Function

Huihua Wu; Hansong Su; Gaohua Liu; Shen Li; Xiao Su

doi:10.3788/LOP56.241502

[1] Tian Y I, Kanade T, Cohn J F. Recognizing action units for facial expression analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 97-115(2001).

[2] Darwin C, Prodger P[M]. The expression of the emotions in man and animals(1998).

[3] Ekman P, Friesen W V. Constants across cultures in the face and emotion[J]. Journal of Personality and Social Psychology, 17, 124-129(1971). http://www.ncbi.nlm.nih.gov/pubmed/5542557

[4] Valstar M F, Mehu M, Jiang B H et al. Meta-analysis of the first facial expression recognition challenge[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part b (Cybernetics), 42, 966-979(2012).

[5] Long X, Su H S, Liu G H et al. A face recognition algorithm based on angular distance loss function and convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 121505(2018).

[6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Advances in Neural Information Processing Systems 25(NIPS 2012), December 3-6, 2012, Lake Tahoe, Nevada, United States. Canada: NIPS(2012).

[7] Simonyan K. -04-10)[2019-04-24]. https:∥arxiv., org/abs/1409, 1556(2015).

[8] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 15523970(2015).

[9] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[10] Kim B K, Lee H, Roh J et al. Hierarchical committee of deep CNNs with exponentially-weighted decision fusion for static facial expression recognition. [C]∥Proceedings of the 2015 ACM on International Conference on Multimodal Interaction - ICMI '15, November 9-13, 2015, Seattle, Washington, USA. New York: ACM, 427-434(2015).

[11] Yu Z D, Zhang C. Image based static facial expression recognition with multiple deep network learning. [C]∥Proceedings of the 2015 ACM on International Conference on Multimodal Interaction-ICMI'15, November 9-13, 2015, Seattle, Washington, USA. New York: ACM, 435-442(2015).

[12] Ng H W, Nguyen V D, Vonikakis V et al. Deep learning for emotion recognition on small datasets using transfer learning. [C]∥Proceedings of the 2015 ACM on International Conference on Multimodal Interaction-ICMI'15, November 9-13, 2015, Seattle, Washington, USA. New York: ACM, 443-449(2015).

[13] Yao A B, Cai D Q, Hu P et al. HoloNet: towards robust emotion recognition in the wild. [C]∥Proceedings of the 18th ACM International Conference on Multimodal Interaction - ICMI 2016, November 12-16, 2016, Tokyo, Japan. New York: ACM, 472-478(2016).

[14] Wen Y D, Zhang K P, Li Z F et al. A discriminative feature learning approach for deep face recognition[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9911, 499-515(2016).

[15] Cai J, Meng Z B, Khan A S et al. Island loss for learning discriminative features in facial expression recognition. [C]∥2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), May 15-19, 2018, Xi'an,China. New York: IEEE, 302-309(2018).

[16] Wang F, Cheng J, Liu W Y et al. Additive margin softmax for face verification[J]. IEEE Signal Processing Letters, 25, 926-930(2018).

[17] Kanade T, Cohn J F, Tian Y L. Comprehensive database for facial expression analysis. [C]∥Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), March 28-30, 2000,Grenoble, France. New York: IEEE, 6577271(2000).

[18] Lucey P, Cohn J F, Kanade T et al. The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. [C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 94-101(2010).

[19] Zhao G Y, Huang X H, Taini M et al. Facial expression recognition from near-infrared videos[J]. Image and Vision Computing, 29, 607-619(2011). http://www.sciencedirect.com/science/article/pii/S0262885611000515

[20] Dhall A. Ramana Murthy O V, Goecke R, et al. Video and image based emotion recognition challenges in the wild. [C]∥Proceedings of the 2015 ACM on International Conference on Multimodal Interaction - ICMI '15, November 9-13, 2015, Seattle, Washington, USA. New York: ACM, 423-426(2015).

[21] Li S, Deng W H, Du J P. Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu,HI,USA. New York: IEEE, 2584-2593(2017).

[22] Chollet F. Xception:deep learning with depthwise separable convolutions. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR),July 21-26, 2017, Honolulu, Hawaii, USA.New York: IEEE, 1251-1258(2017).

[23] Deng J K, Zhou Y X, Zafeiriou S. Marginal loss for deep face recognition. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2006-2014(2017).

[24] Saragih J M, Lucey S, Cohn J F. Face alignment through subspace constrained mean-shifts. [C]∥2009 IEEE 12th International Conference on Computer Vision,September 29-October 2, 2009, Kyoto,Japan. New York: IEEE, 1034-1041(2009).

[25] Simard P Y, Steinkraus D, Platt J C. Best practices for convolutional neural networks applied to visual document analysis. [C]∥Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., August 6-6, 2003, Edinburgh, UK. New York: IEEE(2003).

[26] Zhao L M, Li X, Zhuang Y T et al. Deeply-learned part-aligned representations for person re-identification. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 3239-3248(2017).

[27] Isola P, Zhu J Y, Zhou T H et al. Image-to-image translation with conditional adversarial networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 5967-5976(2017).