Sparse representation and least squares-based classification in face recognition

In this paper we present a novel approach to face recognition. We propose an adaptation and extension to the state-of-the-art methods in face recognition, such as sparse representation-based classification and its extensions. Effectively, our method combines the sparsity-based approaches with additional least-squares steps and exhitbits robustness to outliers achieving significant performance improvement with little additional cost. This approach also mitigates the need for a large number of training images since it proves robust to varying number of training samples.