Joint representation and pattern learning for robust face recognition

Image feature is a significant factor for the success of robust face recognition. Recently sparse representation based classifier (SRC) has been widely applied to robust face recognition by using sparse representation residuals to tolerate disturbed image features (e.g., occluded pixels). In order to deal with more complicated image variations, robust representation based classifier, which estimates feature weights (e.g., low weight values are given to the pixels with big representation residuals), has attracted much attention in recent work. Although these methods have achieved improved performance by estimating feature weights independently, structured information and prior knowledge of image features are ignored in these works, resulting in unsatisfactory performance in some challenging cases. Thus how to better learn image feature weight to fully exploit structure information and prior knowledge is still an open question in robust face recognition. In this paper, we proposed a novel joint representation and pattern learning (JRPL) model, in which the feature pattern weight is simultaneously learned with the representation of query image. Especially a feature pattern dictionary, which captures structured information and prior knowledge of image features, are constructed to represent the unknown feature pattern weight of a query image. An efficient algorithm to solve JRPL was also presented in this paper. The experiments of face recognition with various variations and occlusions on several benchmark datasets clearly show the advantage of the proposed JRPL in accuracy and efficiency.