Local histogram specification using learned histograms for face recognition
In the field of face recognition, most existing preprocessing methods only try to filter the low frequency part of the spectrum of face images to eliminate illumination variations. In this paper, we introduce the Local Histogram Specification (LHS) to preprocess face images using learned histograms. Each local histogram to be specified is learned by estimating the distribution of gray values in the corresponding local region of all normal lighting images in the training set. The proposed method is able to alleviate both the low and high frequency parts of illumination on face images as well as enhance face features lying in the low frequency part. Reasonable window size is also empirically studied. Experimental results on two standard illumination variation datasets demonstrate the effectiveness and stability of our proposed method.