Color Face Recognition using High-Dimension Quaternion-based Adaptive Representation

TL;DR

This paper introduces high-dimension quaternion-based adaptive representation methods for color face recognition, improving upon existing quaternion-based techniques by utilizing a flexible $e_p$-norm minimization to enhance recognition accuracy across various conditions.

Contribution

The paper proposes the HD-QAR method that leverages high-dimensional quaternion representations with adaptive $e_p$-norm minimization, offering improved robustness and accuracy over prior quaternion-based face recognition methods.

Findings

HD-QAR outperforms QCRC and QSRC in recognition accuracy.

The adaptive $e_p$-norm enhances performance across different conditions.

Experimental results demonstrate superior recognition rates compared to state-of-the-art methods.

Abstract

Recently, quaternion collaborative representation-based classification (QCRC) and quaternion sparse representation-based classification (QSRC) have been proposed for color face recognition. They can obtain correlation information among different color channels. However, their performance is unstable in different conditions. For example, QSRC performs better than than QCRC on some situations but worse on other situations. To benefit from quaternion-based $e_2$-norm minimization in QCRC and quaternion-based $e_1$-norm minimization in QSRC, we propose the quaternion-based adaptive representation (QAR) that uses a quaternion-based $e_p$-norm minimization ($1 \le p \le 2$) for color face recognition. To obtain the high dimension correlation information among different color channels, we further propose the high-dimension quaternion-based adaptive representation (HD-QAR). The experimental results demonstrate that the proposed QAR and HD-QAR achieve better recognition rates than QCRC, QSRC and several state-of-the-art methods.