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Smile recognition has recently attracted significant attention due to the increasing accessibility in some digital products. Traditionally, the mouth feature has been extracted for smile recognition. However, it only extracts the features in the mouth region, ignoring the fact that face feature contribute to characterize expressions. In this paper, in order to overcome the drawbacks and make full use of the merits of both features, we propose a novel feature fusion approach for the smile recognition. In our approach, the mouth features are described by HOG descriptor and the face features are represented by Local Binary Pattern (LBP) histograms. Then, the two features are fused by discriminative canonical correlation analysis (DCCA). Robust Support Vector Machine (SVM) classifier is used for classification in our experiment and satisfactory results of experiments based on two databases are obtained finally.

Smile recognition based on the fusion of the face texture feature and the mouth shape feature