Visual information encoding for face recognition : sparse coding vs vector quantization

In this paper, we investigate the problem of visual information encoding for face recognition. We consider two models of information encoding based on sparse coding and vector quantization and compare their performance and computational complexity. The optimal solution is considered from the point of view of the best achievable classification accuracy by minimizing the probability of error under a given class of distortions. The results from the computer simulations confirm that our approach achieves similar performance with state-of-the-art sparse coding based image classification methods but with the considerably lower complexity.