Face recognition using feature extraction based on independent component analysis

We have explored a new method of feature extraction for face recognition. It is based on independent component analysis (ICA), but unlike original ICA, one of the unsupervised learning methods, it is developed to be well suited for classification problems by utilizing class information. By using ICA in solving supervised classification problems, we can obtain new features which are made as independent from each other as possible and which convey the class information faithfully. We have applied this method on Yale Face Databases and AT & T Face Databases and compared the performance with those of conventional methods such as principal component analysis (PCA), Fisher's linear discriminant (FLD), and so on. The experimental results show that for both databases the proposed method outperforms the others.