Rough common vector: A new approach to face recognition
Akihiro Tamura, Qiangfu Zhao · 2007
Face recognition is one of the most fundamental functions for many kinds of intelligent robots. Among many methods proposed for face recognition, linear approaches such as Eigenface or principal component analysis (PCA), Fisher’s linear discriminant (FLD), Fisherface, and common vector (CV), have attracted great attention because of their simplicity. It is known that when the number of training examples is large enough, we should use FLD; otherwise, we should use CV. This paper proposes a rough common vector (RCV) approach. The basic idea of RCV is to divide the feature space into two subspaces. One is spanned by the eigenvectors corresponding to the largest eigenvalues of the within-class scatter matrix, and another is spanned by the eigenvectors corresponding to the smallest eigenvalues. The later plays the role of the null space of the within-class scatter matrix, and is important for extracting useful discriminative features for recognition. RCV can be used regardless the within-class scatter matrix is singular or not. Experimental results on four databases show that RCV outperforms nearest neighbor classifier, Eigenface, Fisherface, and CV in most cases.