Individual-free representation-based classification for facial expression recognition

Abstract

Facial expression recognition (FER) plays a significant role in human–computer interaction. However, in FER applications, the samples are usually corrupted by individual differences, which affect the classification result to some extent. This paper proposes an individual-free representation-based classification, which utilizes the variation training set (VTS) and the virtual variation training set (VVTS) to remit the side-effect caused by individual differences. The VTS and VVTS are both generated from the original training set and show possible variation of the expression. The new approach performs low-rank decomposition-based singular value decomposition for both VTS and VVTS, and then integrates them to determine the label of the query sample. This promising performance is mainly attributed to the fact that VTS and VVTS used in the proposed method can exploit limited original training set to produce a large possible expression variation. Experimental results show that the proposed method can achieve better performance than most of the competitive FER methods, e.g., SVM, SRC, CRC, LRC and the method in Lee et al.

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