Discriminative dictionary learning with low-rank regularization for face recognition

Abstract

We consider learning a discriminative dictionary in sparse representation and specifically focus on face recognition application to improve its performance. This paper presents an algorithm to learn a discriminative dictionary with low-rank regularization on the dictionary. To make the dictionary more discerning, we apply Fisher discriminant function to the coding coefficients with the goal that they have a small ratio of the within-class scatter to between-class scatter. However, noise in the training samples will undermine the discrimination power of the dictionary. To handle this problem, we base on low-rank matrix recovery theory and apply a low-rank regularization on the dictionary. The proposed discriminative dictionary learning with low-rank regularization (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) algorithm is evaluated on several face image datasets in comparison with existing representative dictionary learning and classification algorithms. The experimental results demonstrate its superiority.