Logarithm Difference Based Illumination Invariant Feature for Severe Illumination Variation Face Recognition

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 general logarithm difference model (GLDM) is developed by integrating positive and negative logarithm differences. Then, the multiscale logarithm difference edge-maps (MSLDE) [1] is employed as the test-bed, and the proposed GLDM is introduced into MSLDE to form General MSLDE (GMSLDE). Finally, the performance of GMSLDE is verified on the Extended Yale B and CMU PIE face databases with severe illumination variations. The experimental results indicate that the proposed GLDM can efficiently improve the performance of logarithm difference edge-map against severe illumination variations.