Robust face matching under different lighting conditions

We focus on the problem of comparing face images under different lighting conditions. A new and robust face image matching algorithm is developed based on an accumulated consistency measure of corresponding normalized gradients at face contour locations between two face images. The proposed new image matching approach is motivated by the characteristics of high image gradient along the face contour. To amend the matching problem due to lighting changes between face images, we define a new consistency measure to be the inner product between two normalized gradient vectors at the corresponding locations in two images. The normalized gradient is obtained by dividing the computed gradient vector by a maximal gradient magnitude in a local neighborhood centered at the pixel of computation. Then we compute the summation of the individual consistency measure from normalized gradients at all the contour pixels to be the robust matching measure between two face images. To alleviate the problem due to shadow and intensity saturation, we introduce an intensity weighting function for each individual consistency measure to form a weighted consistency measure. We test the proposed image matching algorithm on the Yale Face Database, which contains 15 persons captured under three very different lighting conditions. The proposed method can achieve satisfactory recognition results in the experiments.