Local Feature Analysis with Class Information

Abstract

In this paper, we propose a new feature extraction method for face recognition. This method is based on Local Feature Analysis (LFA), a local method for face recognition since it constructs kernels detecting local structures of a face. However, LFA has shown some problems for recognition due to the nature of unsupervised learning. Here, we point out the problems of LFA and propose a new feature extraction method with class information to overcome the shortcomings of LFA. Our method consists of three steps. First, using LFA, a set of local structures are extracted. Second, we select some extracted structures that are efficient for recognition. At last, we combine the selected local structures to represent them in a more compact form. This results in new bases which have compromised aspects between kernels of LFA and eigenfaces for face images. Throughout the experiments, our method has shown improvements on the face recognition over the previously proposed methods, LFA, eigenface, and fisherface.