Expression-independent face recognition based on Higher-Order Singular Value Decomposition

Hua Chun Tan*, Yu Jin Zhang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

5 引用 (Scopus)

摘要

In this paper, a new method for extracting expressionindependent face features based on HOSVD (Higher-Order Singular Value Decomposition) is proposed and used for face recognition. In the new method, it is assumed that a facial expression could be represented by the facial expressions in the training set. In addition, the expression with higher similarity to the expression of test person has higher probability to represent the expression of test person. Expression-similarity weighted expression feature, which is the optimal estimation based on Bayesian estimation theory and the assumption, is used to estimate the face feature of the test person. As a result, the estimated face feature can reduce the influence of expression caused by insufficient training data and becomes less expression-dependent, and can be more robust to new expressions. The proposed method has been applied to Japanese Female Facial Expression (JAFFE) database. Expression-independent experimental results show the superiority of proposed method over the existing methods in terms of recognition rate and accumulative recognition rate.

源语言英语
主期刊名Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
2846-2851
页数6
DOI
出版状态已出版 - 2008
活动7th International Conference on Machine Learning and Cybernetics, ICMLC - Kunming, 中国
期限: 12 7月 200815 7月 2008

出版系列

姓名Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
5

会议

会议7th International Conference on Machine Learning and Cybernetics, ICMLC
国家/地区中国
Kunming
时期12/07/0815/07/08

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