Abstract
Regarded as its subtlety, micro-expression is a challenging research problem. In the paper, we propose a relax K-SVD classifier (RK-SVD) for micro-expression recognition. RK-SVD minimizes the variance of sparse coefficients to address the similarity of same classes and the distinctiveness of different classes in sparse coefficients. In addition, reconstruction error and classification error are also considered. 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. We show that RK-SVD can effectively recognize micro-expression under three spontaneous micro-expression datasets including SMIC, CASME, and CASME II.
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Acknowledgements
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., Zhu, J., Yang, Z. et al. Effective micro-expression recognition using relaxed K-SVD algorithm. Int. J. Mach. Learn. & Cyber. 8, 2043–2049 (2017). https://doi.org/10.1007/s13042-017-0684-6
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DOI: https://doi.org/10.1007/s13042-017-0684-6