Abstract
This paper focuses on human facial expression recognition in video sequences. Different from the methods of two-dimensional image recognition and three-dimensional spatial-temporal interest point detection, our approach highlights human facial expression recognition in complex spatial-temporal video datasets. The major challenge in facial expression recognition is how to obtain a feature dictionary from extracted cube pixel windows based on clustering algorithm. In this paper, our contributions are mainly concentrated on two aspects. Firstly, we combine discrete linear filter with key parameters selection procedure to extract 3D cuboids. Secondly, we propose a novel seed spot selection method to optimize K-means clustering algorithm. The proposed algorithms are evaluated on open databases. The results show that our approach can achieve outstanding results and the proposed approach is significantly effective.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of China under Contract 61272052, 61473086, 61672079 and 61601466, in part by PAPD, in part by CICAEET, and in part by the National Basic Research Program of China under Grant 2015CB352501. The work of B. Zhang was supported by the Program for New Century Excellent Talents University within the Ministry of Education, China, and Beijing Municipal Science & Technology Commission Z161100001616005.
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Yang, Y., Yang, B., Wei, W., Zhang, B. (2016). Human Facial Expression Recognition Based on 3D Cuboids and Improved K-means Clustering Algorithm. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_32
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