Random Subspace Two-dimensional LDA for Face Recognition

TL;DR

This paper introduces RS-2DLDA, a face recognition technique that improves accuracy over RS-2DPCA by using more discriminative eigenvectors and a weighting scheme, validated on MORPH-II and ORL datasets.

Contribution

The paper presents a novel RS-2DLDA method that enhances face recognition accuracy by leveraging discriminative eigenvectors and a new weighting scheme.

Findings

RS-2DLDA outperforms RS-2DPCA in accuracy.

The weighting scheme further improves recognition performance.

Effective on MORPH-II and ORL face datasets.

Abstract

In this paper, a novel technique named random subspace two-dimensional LDA (RS-2DLDA) is developed for face recognition. This approach offers a number of improvements over the random subspace two-dimensional PCA (RS2DPCA) framework introduced by Nguyen et al. [5]. Firstly, the eigenvectors from 2DLDA have more discriminative power than those from 2DPCA, resulting in higher accuracy for the RS-2DLDA method over RS-2DPCA. Various distance metrics are evaluated, and a weighting scheme is developed to further boost accuracy. A series of experiments on the MORPH-II and ORL datasets are conducted to demonstrate the effectiveness of this approach.