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.
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Pan, H., Xia, S., Jin, L., Xia, L. (2011). Efficient Face Recognition Fusing Dynamic Morphological Quotient Image with Local Binary Pattern. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_29
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DOI: https://doi.org/10.1007/978-3-642-21498-1_29
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