A robust face recognition through statistical learning of local features

Among various signals that can be obtained from humans, facial image is one of the hottest topics in the field of pattern recognition and machine learning due to its diverse variations. In order to deal with the variations such as illuminations, expressions, poses, and occlusions, it is important to find a discriminative feature which can keep core information of original images as well as can be robust to the undesirable variations. In the present work, we try to develop a face recognition method which is robust to local variations through statistical learning of local features. Like conventional local approaches, the proposed method represents an image as a set of local feature descriptors. The local feature descriptors are then treated as a random samples, and we estimate the probability density of each local features representing each local area of facial images. In the classification stage, the estimated probability density is used for defining a weighted distance measure between two images. Through computational experiments on benchmark data sets, we show that the proposed method is more robust to local variations than the conventional methods using statistical features or local features.