Abstract
The extraction of facial features is the key to micro-expression recognition. This paper puts forward a micro-expression recognition algorithm through multi-feature fusion. In this algorithm, the change of local binary pattern (LBP) feature distribution is correlated with projection error. For fast and accurate detection, the research data were all extracted from professional facial expression databases, the images had basically the same positions in each expression library, and the pure face image was acquired through manual segmentation from the selected expression library. Through comparison and its application in an intelligent classroom environment, it is proved that the proposed algorithm clearly outperforms the original LBP algorithm. The proposed method can be extended to other image recognition and classification problems.
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