Face recognition using entropy weighted patch PCA array under variation of lighting conditions from a single sample image per person

Authors:
Hamidreza Rashidy Kanan;M. Shahram Moin
Affiliations:
Electrical and Computer Engineering Department, Islamic Azad University, Qazvin, Iran;Multimedia Research Group, IT Faculty, Iran Telecom Research Center, Tehran, Iran
Venue:
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Year:
2009

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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.