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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