General logarithm difference model for severe illumination variation face recognition.

Logarithm difference is the subtraction of the center pixel and its neighbor in the face local region. The commonly-used assumption of the illumination invariant measure is that illumination intensities of neighbor pixels are approximately equal in the face local region. However, logarithm difference based illumination invariant measure performs unsatisfactorily under severe illumination variations, since severe varying illumination cause differences of illumination intensities are large in the face local region. In this paper, the general logarithm difference model (GLDM) is proposed to tackle severe illumination variations. In spired by the fact that a logarithm difference is the subtraction of two neighbor pixels, which may be a positive or negative numerical value, we divide a face local region into a positive region and a negative region, and the GLDM is developed by integrating positive and negative logarithm differences. Then, the multiscale logarithm difference edge-maps (MSLDE) [7] is employed as the test-bed, and the proposed GLDM is introduced into MSLDE to form General MSLDE (GMSLDE). Further, the proposed GMSLDE method is integrated with the advanced deep learning model VGG to obtain GMSLDE-VGG. Finally, the performance of GMSLDE and GMSLDE-VGG are verified not only on the Extended Yale B and CMU PIE face databases with severe illumination variations, but also on the LFW and our self-built Driver face databases with moderate illumination variations. The experimental results indicate that the proposed GLDM can efficiently improve the performance of logarithm difference edge-map against illumination variations, especially for severe illumination variations. [ABSTRACT FROM AUTHOR]