Face recognition with illumination distinction description

Varying lighting conditions may cause great face image appearance variance and hence seriously distort the distributions of within and between class distances, but these distributions are exactly the foundation of successful automatic recognition. In this paper, a dynamic illumination distinction description strategy is put forward to compensate for the lighting difference between probe and gallery faces. After enrolling this description, face image distances will more faithfully reflect face identity difference. We also introduce an algorithm framework to integrate both illumination distinction description computation and face recognition into once ℓ₁-optimization. The effectiveness of proposed approach is verified on the Extended Yale B dataset.