NDLI: An Automatic Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree

Content Provider	IEEE Xplore Digital Library
Author	Qinzhen Xu Pinzheng Zhang Wenjiang Pei Luxi Yang Zhenya He
Copyright Year	2007
Description	Author affiliation: Sch. of Inf. Sci., Southeast Univ., Nanjing, China (Qinzhen Xu)
Abstract	Automatic facial expression recognition is the kernel part of emotional information processing. This paper dedicates to develop an automatic facial expression recognition approach based on confusion-crossed support vector machine tree (CSVMT) to improve recognition accuracy and robustness. After the pseudo-Zernike moment features were extracted, they were used to train a CSVMT for automatic recognition. The structure of CSVMT enables the model to divide the facial recognition problem into sub-problems according to the teacher signals, so that it can solve the sub-problems in decreased complexity in different tree levels. In the training phase, those sub-samples assigned to two internal sibling nodes perform decreasing confusion cross, thus, the generalization ability of CSVMT for recognition of facial expression is enhanced. The compared results on Cohn-Kanade facial expression database also show that the proposed approach appeared higher recognition accuracy and robustness than other approaches.
File Size	4526304
File Format	PDF
ISBN	1424407273
ISSN	15206149
DOI	10.1109/ICASSP.2007.365985
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Support vector machines Facial features Robustness Feature extraction Emotion recognition Psychology Image recognition Humans Pattern recognition Artificial intelligence Image classification
Content Type	Text
Resource Type	Article

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