Face recognition using local multi dimensional statistics

Though numerous approaches have been proposed for face recognition. In this paper we propose a novel face recognition approach based on adaptively weighted patch local statistic in multi dimensional (LMDS) when only one exemplar image per person is available. In this approach, a face image is decomposed into a set of equal-sized patches in a non-overlapping way. In order to obtain local multi dimensional statistic features in each patch, we calculated mean and standard deviation of all pixels along some directions. An adaptively weighting scheme is used to assign proper weights to each LMDS features to adjust the contribution of each local area of a face in terms of the quantity of identity information that a patch contains. An extensive experimental investigation is conducted using AR face databases covering face recognition under controlled/ideal conditions and different facial expressions. The system performance is compared with the performance of four benchmark approaches. The encouraging experimental results demonstrate that our approach can be used for face recognition and patch-based local statistic features provides a novel way for face.