Supervised transform learning for face recognition

Abstract

In this paper we investigate transform learning and apply it to face recognition problem. The focus is to find a transformation matrix that transforms the signal into a robust to noise, discriminative and compact representation. We propose a method that finds an optimal transform under the above constrains. The non-sparse variant of the presented method has a closed form solution whereas the sparse one may be formulated as a solution to a sparsity regularized problem. In addition we give a generalized version of the proposed problem and we propose a prior on the data distribution across the dimensions in the transform domain. Supervised transform learning is applied to the MVQ [10] method and is tested on a face recognition application using the YALE B database. The recognition rate and the robustness to noise is superior compared to the original MVQ based on κ-means.