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Illumination Normalization Using Self-lighting Ratios for 3D2D Face Recognition
Xi Zhao, Shishir K. Shah, and Ioannis A. Kakadiaris
Computational Biomedicine Laboratory, Department of Computer Science, University of Houston, 4800 Calhoun, Houston, TX 77204, USAzhaoxi1@gmail.com
shah@cs.uh.edu
ioannisk@uh.edu
Abstract. 3D2D face recognition is beginning to gain attention from the research community. It takes advantage of 3D facial geometry to normalize the head pose and registers it into a canonical 2D space. In this paper, we present a novel illumination normalization approach for 3D2D face recognition which does not require any training or prior knowledge on the type, number, and direction of the lighting sources. Estimated using an image-specific filtering technique in the frequency domain, a self-lighting ratio is employed to suppress illumination differences. Experimental results on the UHDB11 and FRGC databases indicate that the proposed approach improves the performance significantly for face images with large illumination variations.
Keywords: Lighting ratio, illumination suppression, 3D2D face recognition
LNCS 7584, p. 220 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012