Expression-independent face recognition based on higher-order singular value decomposition

In this paper, a new method for extracting expression-independent 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 traini...

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Bibliographic Details
Main Authors: Hua-Chun Tan, Yu-Jin Zhang
Format: Conference Proceeding
Language:English
Subjects:
Expression
Face
Face recognition
HOSVD
Machine learning
Support vector machine classification
Tensile stress
Training
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Summary:In this paper, a new method for extracting expression-independent 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.
ISSN:2160-133X
DOI:10.1109/ICMLC.2008.4620893