A Facial Expression Recognition Approach Based on Novel Support Vector Machine Tree

Authors:
Qinzhen Xu;Pinzheng Zhang;Luxi Yang;Wenjiang Pei;Zhenya He
Affiliations:
School of Information Science and Engineering, Southeast University, Nanjing, 210096, China;School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China;School of Information Science and Engineering, Southeast University, Nanjing, 210096, China;School of Information Science and Engineering, Southeast University, Nanjing, 210096, China;School of Information Science and Engineering, Southeast University, Nanjing, 210096, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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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 a novel support vector machine tree, which performs feature selection at each internal node, to improve recognition accuracy and robustness. After the Pseudo-Zernike moment features were extracted, they were used to train a support vector machine tree for automatic recognition. The structure of a support vector machine 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 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.