Efficient face recognition fusing dynamic morphological quotient image with local binary pattern

Authors:
Hong Pan;Siyu Xia;Lizuo Jin;Liangzheng Xia
Affiliations:
School of Automation, Southeast University, Nanjing, China;School of Automation, Southeast University, Nanjing, China;School of Automation, Southeast University, Nanjing, China;School of Automation, Southeast University, Nanjing, China
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Year:
2011

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Abstract

In this paper, we propose a novel illumination normalized Local Binary Pattern (LBP)-based algorithm for face recognition under varying illumination conditions. The proposed DMQI-LBP algorithm fuses illumination normalization, using the Dynamic Morphological Quotient Image (DMQI), into the current LBP-based face recognition system. So it makes full use of advantages of illumination compensation offered by the quotient image, estimated with a dynamic morphological close operation, as well as the powerful discrimination ability provided by the LBP descriptor. Evaluation results on the Yale face database B indicate that the proposed DMQI-LBP algorithm significantly improve the recognition performance (by 5% for the first rank) of the original raw LBP-based system for face recognition with severe lighting variations. Furthermore, our algorithm is efficient and simple to implement, which makes it very suitable for real-time face recognition.