Subspace evolution analysis for face representation and recognition

This paper develops a novel framework that is capable of dealing with small sample size problem posed to subspace analysis methods for face representation and recognition. In the proposed framework, three aspects are presented. The first is the proposal of an iterative sampling technique. The second is adopting divide–conquer–merge strategy to incorporate the iterative sampling technique and subspace analysis method. The third is that the essence of 2D PCA is further explored. Experiments show that the proposed algorithm outperforms the traditional algorithms.