Robust Face Recognition Based on Part-Based Localized Basis Images

In order for a subspace projection based method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective partbased local representation method using ICA architecture I basis images that is robust to local distortion and partial occlusion. The proposed representation only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of “recognition by parts.” We have contrasted our representation with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that our representation performs better than PCA, ICA architectureI, ICA architectureII, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.