期刊
PATTERN RECOGNITION
卷 64, 期 -, 页码 399-406出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2016.12.002
关键词
Zernike moment; Facial expression; Motion history image; Entropy; Feature extraction
资金
- China Scholarship Council / Warwick Joint Scholarship [201206710046]
A dynamic descriptor facilitates robust recognition of facial expressions in video sequences. The current two main approaches to the recognition are basic emotion recognition and recognition based on facial action coding system (FACS) action units. In this paper we focus on basic emotion recognition and propose a spatio-temporal feature based on local Zernike moment in the spatial domain using motion change frequency. We also design a dynamic feature comprising motion history image and entropy: To recognise a facial expression, a weighting strategy based on the latter feature and sub-division of the image frame is applied to the former to enhance the dynamic information of facial expression, and followed by the application of the classical support vector machine. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme demonstrate that the integrated framework achieves a better performance than using individual descriptor separately. Compared with six state-of-arts methods, the proposed framework demonstrates a superior performance.
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