TY - GEN
T1 - Expression-independent face recognition based on Higher-Order Singular Value Decomposition
AU - Tan, Hua Chun
AU - Zhang, Yu Jin
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Expression
KW - Face recognition
KW - HOSVD
UR - http://www.scopus.com/inward/record.url?scp=57849100732&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2008.4620893
DO - 10.1109/ICMLC.2008.4620893
M3 - Conference contribution
AN - SCOPUS:57849100732
SN - 9781424420964
T3 - Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
SP - 2846
EP - 2851
BT - Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
T2 - 7th International Conference on Machine Learning and Cybernetics, ICMLC
Y2 - 12 July 2008 through 15 July 2008
ER -