Abstract
2DPCA, which is one of the most important face recognition methods, is relatively sensitive to substantial variations in light direction, face pose, and facial expression. In order to improve the recognition performance of the traditional 2DPCA, a new 2DPCA algorithm based on the fuzzy theory is proposed in this paper, namely, the fuzzy 2DPCA (F2DPCA). In this method, applying fuzzy K-nearest neighbor (FKNN), the membership degree matrix of the training samples is calculated, which is used to get the fuzzy means of each class. The average of fuzzy means is then incorporated into the definition of the general scatter matrix with anticipation that it can improve classification result. The comprehensive experiments on the ORL, the YALE, and the FERET face database show that the proposed method can improve the classification rates and reduce the sensitivity to variations between face images caused by changes in illumination, face expression, and face pose.
Highlights
Face recognition is one kind of biometrics, aiming at capturing and use of behavioral or physiological characteristics for individual verification or personal identification
The fuzzy membership degree and the class centers are obtained through the Fuzzy K-Nearest Neighbor (FKNN) algorithm [14]
The idea of the fuzzy 2DPCA is that the means of each class are calculated with fuzzy membership degrees matrix of all training samples by (6) firstly, and we average all class means to get the center of all training samples
Summary
Face recognition is one kind of biometrics, aiming at capturing and use of behavioral or physiological characteristics for individual verification or personal identification. Based on what have mentioned above, in order to make full use of the class information and the distribution information of the samples to make an accurate estimate of the training samples mean in the definition of the 2DPCA model, we incorporate the fuzzy theory and the class information into the computation of the mean matrix. We call this method fuzzy 2DPCA (F2DPCA) algorithm.
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