# Non-negative representation based discriminative dictionary learning for face recognition

## TL;DR

This paper introduces a novel discriminative dictionary learning algorithm for face recognition that uses non-negative representation to enhance class discrimination and improve recognition accuracy.

## Contribution

It proposes a unified model combining non-negative representation, discriminative dictionary learning, and classifier training for improved face classification.

## Key findings

- Outperforms state-of-the-art methods on benchmark face datasets.
- Produces sparse, discriminative, and class-specific dictionary atoms.
- Enhances face recognition accuracy through novel constraints and regularization.

## Abstract

In this paper, we propose a non-negative representation based discriminative dictionary learning algorithm (NRDL) for multicategory face classification. In contrast to traditional dictionary learning methods, NRDL investigates the use of non-negative representation (NR), which contributes to learning discriminative dictionary atoms. In order to make the learned dictionary more suitable for classification, NRDL seamlessly incorporates nonnegative representation constraint, discriminative dictionary learning and linear classifier training into a unified model. Specifically, NRDL introduces a positive constraint on representation matrix to find distinct atoms from heterogeneous training samples, which results in sparse and discriminative representation. Moreover, a discriminative dictionary encouraging function is proposed to enhance the uniqueness of class-specific sub-dictionaries. Meanwhile, an inter-class incoherence constraint and a compact graph based regularization term are constructed to respectively improve the discriminability of learned classifier. Experimental results on several benchmark face data sets verify the advantages of our NRDL algorithm over the state-of-the-art dictionary learning methods.