Person-independent facial expression recognition via hierarchical classification
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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 method 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.
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Xue, Mingliang; Liu, Wan-Quan; Li, Ling (2014)Recent research has shown improved performance by embedding the colour information in the process of facial expression recognition (FER). However, the RGB colour space may not always be the most desirable space for facial ...
Xue, M.; Mian, A.; Liu, Wan-Quan; Li, Ling (2015)This paper addresses the problem of person-independent 4D facial expression recognition. Unlike the majority of existing works, we propose to extract spatio-temporal features in 4D data (3D expression sequences changing ...
Xue, Mingliang; Mian, A.; Liu, Wan-Quan; Li, Ling (2014)Facial expressions form a significant part of our nonverbal communications and understanding them is essential for effective human computer interaction. Due to the diversity of facial geometry and expressions, automatic ...