Person-independent facial expression recognition via hierarchical classification

Automatically recognizing facial expressions presents an active and challenging problem in computer vision and pattern classification. The person-independent case is even more challenging. In this paper, we propose a hierarchical approach to achieve person-independent facial expression recognition. Specifically, the expressions that are easily confused together are merged into one class and join the remaining prototypic expressions in the first tier classification; the expressions in the merged class are then separated in the second tier. Support Vector Machine is adopted as the classifier in both tiers, with the LBP and displacement features in the first tier as well as mouth and eyebrows features in the second tier. The proposed metLhod is tested on the Cohn-Kanade Extended (CK+) dataset and evaluated in terms of a confusion matrix. The person-independent experiments demonstrate the effectiveness of the proposed hierarchical classifier in improving recognition accuracy and eliminating confusions.