An Improved Robust Sparse Coding for Face Recognition with Disguise

Abstract

Robust vision-based face recognition is one of most challenging tasks for robots. Recently the sparse representation-based classification (SRC) has been proposed to solve the problem. All training samples without disguise are used to compose an over-complete dictionary, and the testing sample with disguise is represented by the dictionary with a sparse coding coefficients plus an error. The coding residuals between the sample and each class of training samples are measured and the minimum of them is the identified class to which the sample belongs. The robust sparse coding (RSC) seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, so it is more robust to disguise. However, the iteratively algorithm to solve RSC is high time consuming. In this paper, we propose an improved robust sparse coding (iRSC) algorithm for practical application conditions. During iterations, the dictionary is reduced by eliminating the objects with larger coding residuals. The over-complete property of dictionary is not affected. Experiments on AR face database demonstrate that the coding is sparser and the efficiency is higher in iRSC.