Facial expression recognition via learning deep sparse autoencoders

Neurocomputing

Volumn 273, Issue , 2018, Pages 643-649

  Zeng, Nianyin ^a   Zhang, Hong ^a   Song, Baoye ^b   Liu, Weibo ^c   Li, Yurong ^d,e   Dobaie, Abdullah M ^f  

^a XIAMEN UNIVERSITY   (China)

^b SHANDONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c BRUNEL UNIVERSITY   (United Kingdom)

^d FUZHOU UNIVERSITY   (China)

^e Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology   (China)

^f KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Deep architecture; Facial expression recognition; High dimensional feature; Histogram of oriented gradients (HOG); Sparse autoencoders

Indexed keywords

LEARNING SYSTEMS; PATTERN RECOGNITION;

AUTOENCODERS; DEEP ARCHITECTURES; FACIAL EXPRESSION RECOGNITION; HIGH DIMENSIONAL FEATURE; HISTOGRAM OF ORIENTED GRADIENTS (HOG);

FACE RECOGNITION;

ACCURACY; ANGER; ARTICLE; DISGUST; FACIAL EXPRESSION; FACIAL RECOGNITION; FACIES; FEAR; GEOMETRY; HAPPINESS; HUMAN; PERCEPTIVE DISCRIMINATION; PRIORITY JOURNAL; SADNESS; SATISFACTION;

EID: 85029156229     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2017.08.043     Document Type: Article

References (49)

1. Anderson, K., Owan, P.M., A real-time automated system for the recognition of human facial expressions. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36:1 (2006), 96–105.
2. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., Montreal, U., Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst., 19, 2007, 153.
3. Chen, D., Cao, X., Wen, F., Sun, J., Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013, 3025–3032.
4. Chen, H., Liang, J., Wang, Z., Pinning controllability of autonomous Boolean control networks. Sci. China Inf. Sci., 59(7), 2016, 070107, 10.1007/s11432-016-5579-8.
5. Cohen, I., Sebe, N., Garg, A., Chen, L., Huang, T., Facial expression recognition from video sequences: temporal and static modeling. Comput. Vision Image Understanding 91:1–2 (2003), 160–187.
6. Cootes, T., Edwards, G., Taylor, C., Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23:6 (2001), 681–685.
7. Dalal, N., Triggs, B., Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 886–893.
8. Dempster, A., Laird, N., Rubin, D., Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39:1 (1977), 1–38.
9. Ding, D., Wang, Z., Ho, D.W.C., Wei, G., Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks. Automatica 78 (2017), 231–240.
10. Ding, D., Wang, Z., Wei, G., Alsaadi, F.E., Event-based security control for discrete-time stochastic systems. IET Control Theory Appl. 10:15 (2016), 1808–1815.
11. Ekman, P., Friesen, W., Constants across cultures in the face and emotion. J. Pers. Social Psychol. 17:2 (1971), 124–129.
12. Fasel, B., Luettin, J., Automatic facial expression analysis: a survey. Pattern Recognit. 36:1 (2003), 259–275.
13. Gritti, T., Shan, C., Jeanne, V., Braspenning, R., Local features based facial expression recognition with face registration errors. The 8th IEEE International Conference on Automatic Face and Gesture Recognition, 2008, 1–8.
14. Hinton, G., Salakhutdinov, R., Reducing the dimensionality of data with neural networks. Science 313:5786 (2006), 504–507.
15. Huang, X., Zhao, G., Zheng, W., Pietikainen, M., Spatiotemporal local monogenic binary patterns for facial expression recognition. IEEE Signal Process. Lett. 19:5 (2012), 243–246.
16. Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P., Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10 (2009), 1–40.
17. Li, Z., Imai, J., Kaneko, M., Facial expression recognition using facial-component-based bag of words and PHOG descriptors. J. Inst. Image Inf. Telev. Eng. 64:2 (2010), 230–236.
18. Li, J., Lam, E., Facial expression recognition using deep neural networks. 2015 IEEE International Conference on Imaging Systems and Techniques, 2015, 1–6.
19. Liu, D., Liu, Y., Alsaadi, F.E., A new framework for output feedback controller design for a class of discrete-time stochastic nonlinear system with quantization and missing measurement. Int. J. Gen. Syst. 45:5 (2016), 517–531.
20. Liu, D., Nocedal, J., On the limited memory BFGS method for large scale optimization. Math. Prog. 45:1 (1989), 503–528.
21. Liu, S., Wei, G., Song, Y., Liu, Y., Extended Kalman filtering for stochastic nonlinear systems with randomly occurring cyber attacks. Neurocomputing 207 (2016), 708–716.
22. Liu, Y., Liu, W., Obaid, M.A., Abbas, I.A., Exponential stability of Markovian jumping Cohen-Grossberg neural networks with mixed mode-dependent time-delays. Neurocomputing 177 (2016), 409–415.
23. Lucey, P., Cohn, J., Kanade, T., Saragih, J., The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion specified expression. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, 94–101.
24. ∞ filtering for a class of two-dimensional uncertain fuzzy systems with randomly occurring mixed delays. IEEE Trans. Fuzzy Syst. 25:1 (2017), 70–83.
25. ∞ control for 2-D fuzzy systems with interval time-varying delays and missing measurements. IEEE Trans. Cybern. 47:2 (2017), 365–377.
26. Masci, J., Meier, U., Ciresan, D., Schmidhuber, J., Stacked convolutional auto-encoders for hierarchical feature extraction. 21st International Conference on Artificial Neural Networks, 2011, 14–17.
27. Pantic, M., Rothkrantz, L., Automatic analysis of facial expressions: the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 22:12 (2000), 1424–1445.
28. Pantic, M., Patras, I., Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36:2 (2006), 433–449.
29. Ranzato, M., Huang, F., Boureau, Y., LeCun, Y., Unsupervised learning of invariant feature hierarchies with applications to object recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007, 1–8.
30. Ranzato, M., Boureau, Y., Cun, Y., Sparse feature learning for deep belief networks. Adv. Neural Inf. Process. Syst. 20 (2008), 873–880.
31. Shan, C., Gong, S., Owan, P.M., Facial expression recognition based on local binary patterns: a comprehensive study. Image Vision Comput. 27:6 (2009), 803–816.
32. Shin, H., Orton, M., Collins, D., Doran, S., Leach, M., Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans. Pattern Anal. Mach. Intell. 35:8 (2012), 1930–1943.
33. Tian, Y., Kanade, T., Cohn, J., Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity. Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002, 229–234.
34. Tian, Y., Kanade, T., Cohn, J., Facial expression analysis. Handbook of Face Recognition, 2005, Springer.
35. Valstar, M., Pantic, M., Combined support vector machines and hidden Markov models for modeling facial action temporal dynamics. Human-Comput. Interact. 4796 (2007), 118–127.
36. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P., Extracting and composing robust features with denoising autoencoders. ICML’08 Proceedings of the 25th International Conference on Machine Learning, 2008, 1096–1103.
37. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P., Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 (2010), 3371–3408.
38. Wang, L., Wang, Z., Huang, T., Wei, G., An event-triggered approach to state estimation for a class of complex networks with mixed time delays and nonlinearities. IEEE Trans. Cybern. 46:11 (2016), 2497–2508.
39. Wen, C., Cai, Y., Liu, Y., Wen, C., A reduced-order approach to filtering for systems with linear equality constraints. Neurocomputing 193 (2016), 219–226.
40. Wiley, J., Ltd, S., Principal component analysis. Wiley StatsRef: Statistics Reference Online, 2014.
41. Zeng, N., Wang, Z., Zhang, H., Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter. Sci. China Inf. Sci., 59(11), 2016, 112204, 10.1007/s11432-016-0280-9.
42. Zeng, N., Wang, Z., Zhang, H., Alsaadi, F.E., A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay. Cognit. Comput. 8:2 (2016), 143–152.
43. Zeng, N., Zhang, H., Li, Y., Liang, J., Dobaie, A.M., Denoising and deblurring gold immunochromatographic strip images via gradient projection algorithms. Neurocomputing 247 (2017), 165–172.
44. Zeng, N., Zhang, H., Liu, W., Liang, J., Alsaadi, F.E., A switching delayed PSO optimized extreme learning machine for short-term load forecasting. Neurocomputing 240 (2017), 175–182.
45. Zeng, N., Wang, Z., Zhang, H., Liu, W., Alsaadi, F., Deep belief networks for quantitative analysis of a gold immunochromatographic strip. Cognit. Comput. 8:4 (2016), 684–692.
46. Zeng, Z., Pantic, M., Roisman, G., Huang, T., A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31:1 (2008), 39–58.
47. Zhang, J., Ma, L., Liu, Y., Passivity analysis for discrete-time neural networks with mixed time-delays and randomly occurring quantization effects. Neurocomputing 216 (2016), 657–665.
48. Zhang, W., Wang, Z., Liu, Y., Ding, D., Alsaadi, F.E., Event-based state estimation for a class of complex networks with time-varying delays: a comparison principle approach. Phys. Lett. A 381:1 (2017), 10–18.
49. Zhang, W., Shan, S., Chen, X., Gao, W., Are Gabor phases really useless for face recognition. Pattern Anal. Appl. 12:3 (2009), 301–307.