Face recognition using Entropy Weighted Patch PCA Array under variation of lighting conditions from a single sample image per person

Abstract

The sensitivity to illumination changes is one of the most important issues for the evaluation of face recognition systems. In this paper, we propose a new approach to recognize face images under variation of lighting conditions when only one sample image per person is available. In this approach, a face image is represented as an array of Patch PCA (PPCA) extracted from a partitioned face image containing information of local regions instead of holistic information of a face. In order to adjust the contribution of each local region of a face in terms of the richness of identity information, an entropy-based weighting technique is utilized to assign proper weights to PPCA features. The encouraging experimental results using AR face database demonstrate that the proposed method provides a new solution to the problem of robustly recognizing face images under different lighting conditions in single model databases.