Sparse representation for face recognition based on discriminative low-rank dictionary learning

TLDR

Sparse representation seeks the sparsest coefficients to represent a test signal as a linear combination of bases in an over‑complete dictionary, and low‑rank matrix recovery motivates treating data from the same pattern as linearly correlated so that the dictionary is approximately low‑rank. The paper proposes a discriminative low‑rank dictionary learning algorithm for sparse representation. The algorithm jointly optimizes sparse coefficients, class discrimination, and rank minimization in the dictionary, and is applied to face recognition. Experiments show that the proposed method outperforms previous dictionary learning approaches.

Abstract

In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data points as column vectors of a dictionary, then the dictionary should be approximately low-rank. An objective function with sparse coefficients, class discrimination and rank minimization is proposed and optimized during dictionary learning. We have applied the algorithm for face recognition. Numerous experiments with improved performances over previous dictionary learning methods validate the effectiveness of the proposed algorithm.