Abstract
Micro-expression recognition has been a challenging problem in computer vision due to its subtlety, which are often hard to be concealed. In the paper, a relaxed K-SVD algorithm (RK-SVD) to learn sparse dictionary for spontaneous micro-expression recognition is proposed. In RK-SVD, the reconstruction error and the classification error are considered, while the variance of sparse coefficients is minimized to address the similarity of same classes and the distinctiveness of different classes. The optimization is implemented by the K-SVD algorithm and stochastic gradient descent algorithm. Finally a single overcomplete dictionary and an optimal linear classifier are learned simultaneously. Experimental results on two spontaneous micro-expression databases, namely CASME and CASME II, show that the performance of the new proposed algorithm is superior to other state-of-the-art algorithms.
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References
Li, X., Pfister, T., Huang, X., Zhao, G., Pietikainen, M.: A spontaneous microexpression database: inducement, collection and baseline. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6 (2013)
Yan, W.J., Wu, Q., Liu, Y.J., Wang, S.J., Fu, X.: CASME database: a dataset of spontaneous micro-expressions collected from neutralized faces. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–7 (2013)
Yan, W.J., Li, X., Wang, S.J., Zhao, G., Liu, Y.J., Chen, Y.H., Fu, X.: CASME II: an improved spontaneous micro-expression database and the baseline evaluation. PloS ONE 9(1), e86041 (2014)
Wang, Y., See, J., Phan, R.C.-W., Oh, Y.-H.: Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition. PLoS ONE 10(5), e0124674 (2015)
Polikovsky, S., Kameda, Y., Ohta, Y.: Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor. In: 3rd International Conference on Crime Detection and Prevention, pp. 1–6. IET (2009)
Wang, S.J., Chen, H.L., Yan, W.J., Chen, Y.H., Fu, X.: Face recognition and micro-expression based on discriminant tensor subspace analysis plus extreme learning machine. Neural Process. Lett. 39, 25–43 (2013)
Pfister, T., Li, X., Zhao, G., Pietikainen, M.: Recognising spontaneous facial micro-expressions. In: 12th IEEE International Conference on Computer Vision, pp. 1449–1456. IEEE (2011)
Zhang, Q., Li, B.X.: Discriminative k-svd for dictionary learning in face recognition. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR) (2010)
Bradley, D., Bagnell, J.: Differential sparse coding. In: Proceedings Conference on Neural Information Processing Systems (2008)
Mairal, J., Bach, F., Ponce, J.: Task-driven dictionary learning. PLoS ONE 34(4), 791–804 (2012)
Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1932–1939. IEEE (2009)
Acknowledgement
This work is partially supported by the Project funded by China Postdoctoral Science Foundation Under grant No. 2014M5615556, supported by the National Science Foundation of China (61273300, 61232007) and Jiangsu Natural Science Funds for Distinguished Young Scholar (BK20140022). And, it is also partially supported by grants 15KJB520024 from Jiangsu University Natural Science Funds, supported by grants KFKT2014B18 from the State Key Laboratory for Novel Software Technology from Nanjing University, supported by the Collaborative Innovation Center of Wireless Communications Technology, grants 2015NXY05 from Nanjing Xiaozhuang University. Finally, the authors would like to thank the anonymous reviewers for their constructive advice.
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Zheng, H., Geng, X., Yang, Z. (2016). A Relaxed K-SVD Algorithm for Spontaneous Micro-Expression Recognition. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_58
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DOI: https://doi.org/10.1007/978-3-319-42911-3_58
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