The nearest-farthest subspace classification for face recognition

Abstract

The nearest subspace (NS) classification is an efficient method to solve face recognition problem by using the linear regression technique. This method is based on the assumption that face images from a specific subject class tend to span a unique subspace, i.e. a class-specific subspace. Then, a test image has the shortest distance from its own class-specific subspace. In this paper, we present a novel idea for face recognition. This idea considers that a test face image should be far from the farthest subspace (FS) spanned by all training images except the images from the class of this test image. Based on this idea, we propose the FS classifier for face recognition. In our opinion, NS and FS classifiers take advantages of different characteristics of the class-specific subspace. NS classifier exploits the relationship between a test image and a single class while FS classifier measures relationship between this test image and the rest classes. Consequently, we propose the nearest-farthest subspace (NFS) classifier which exploits the both relationships to classify a test image. The comparisons with NS classifier and other state-of-the-art methods on four famous public face databases demonstrate the good performance of FS and NFS.