Extended Two-Dimensional PCA for Efficient Face Representation and Recognition

TL;DR

This paper introduces Extended Two-Dimensional PCA (E2DPCA), a novel face recognition method that enhances 2DPCA by incorporating more covariance information, leading to improved accuracy and efficiency.

Contribution

E2DPCA extends 2DPCA by including a radius of diagonals around the main diagonal, unifying PCA and 2DPCA, and allowing better trade-offs between accuracy and computational complexity.

Findings

E2DPCA outperforms 2DPCA in recognition accuracy.

E2DPCA reduces recognition time.

Parameter r controls accuracy-compression trade-off.

Abstract

In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is proposed which is an extension to the original 2DPCA. We state that the covariance matrix of 2DPCA is equivalent to the average of the main diagonal of the covariance matrix of PCA. This implies that 2DPCA eliminates some covariance information that can be useful for recognition. E2DPCA instead of just using the main diagonal considers a radius of r diagonals around it and expands the averaging so as to include the covariance information within those diagonals. The parameter r unifies PCA and 2DPCA. r = 1 produces the covariance of 2DPCA, r = n that of PCA. Hence, by controlling r it is possible to control the trade-offs between recognition accuracy and energy compression (fewer coefficients), and between training and recognition complexity. Experiments on ORL face database show improvement in both recognition accuracy and recognition time over the original 2DPCA.