Feature extraction for classification problems and its application to face recognition

This study investigates a new method of feature extraction for classification problems. The method is based on the independent component analysis (ICA). However, unlike the original ICA, one of the unsupervised learning methods, it is developed for classification problems by utilizing class information. The proposed method is an extension of our previous work on binary-class problems to multi-class problems. It treats the class labels as input features in order to produce two sets of new features: one that carries much information on the class labels and the other that is irrelevant to the class. The learning rule for this method is obtained using the stochastic gradient method to maximize the likelihood of the observed data. Among the new features, using only class-relevant ones, the dimension of the feature space can be greatly reduced in line with the principle of parsimony, resulting better generalization. This method was applied to recognize face identities and facial expressions using various databases such as the Yale, AT&T (former ORL), Color FERET face databases and so on. The performance of the proposed method was compared with those of conventional methods such as the principal component analysis (PCA), Fisher's linear discriminant (FLD), etc. The experimental results show that the proposed method performs well for face recognition problems.