2DR1-PCA and 2DL1-PCA: two variant 2DPCA algorithms based on none L2 norm

TL;DR

This paper introduces two new face recognition algorithms, 2DR1-PCA and 2DL1-PCA, based on R1 and L1 norms, which are more robust to outliers than traditional 2DPCA.

Contribution

The paper proposes two novel 2DPCA variants utilizing R1 and L1 norms, enhancing robustness against outliers in face recognition tasks.

Findings

Both methods outperform traditional 2DPCA on ORL, YALE, and XM2VTS databases.

The proposed algorithms demonstrate increased robustness to outliers.

Experimental results confirm improved recognition accuracy.

Abstract

In this paper, two novel methods: 2DR1-PCA and 2DL1-PCA are proposed for face recognition. Compared to the traditional 2DPCA algorithm, 2DR1-PCA and 2DL1-PCA are based on the R1 norm and L1 norm, respectively. The advantage of these proposed methods is they are less sensitive to outliers. These proposed methods are tested on the ORL, YALE and XM2VTS databases and the performance of the related methods is compared experimentally.