Sparse Representation by Adding Noisy Duplicates for Enhanced Face Recognition: An Elastic Net Regularization Approach

Abstract

Sparse representation for robust face recognition is a novel concept in the pattern analysis and machine learning community. Through the l1-minimization model, representing a test sample as the sparse combination of the training dictionary can effectively achieve facial images classification. However, when the number of training samples is relatively small, it is insufficient to give the test sample a sparse representation so that the recognition performance degenerates seriously. In this paper, we present a novel approach that employs the Elastic Net regularized regression model. Experimental results on several databases show that the proposed strategy improves the recognition accuracy.