# Face Recognition using Multi-Modal Low-Rank Dictionary Learning

## TL;DR

This paper introduces a multi-modal low-rank dictionary learning approach for face recognition that effectively handles occlusion and illumination variations by fusing raw pixel data with illumination-invariant features.

## Contribution

It presents a novel multi-modal structured low-rank dictionary learning method that improves robustness and discriminability in face recognition under challenging conditions.

## Key findings

- Outperforms existing methods on various datasets.
- Robust to severe illumination changes and occlusion.
- Effective with limited training samples.

## Abstract

Face recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations. Recently several low-rank dictionary learning methods have been proposed and achieved promising results for noisy observations. While these methods are mostly developed for single-modality scenarios, recent studies demonstrated the advantages of feature fusion from multiple inputs. We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, using raw pixels of face images and their illumination invariant representation. The proposed method learns robust and discriminative representations from contaminated face images, even if there are few training samples with large intra-class variations. Extensive experiments on different datasets validate the superior performance and robustness of our method to severe illumination variations and occlusion.