Horizontal and vertical 2DPCA-based discriminant analysis for face verification on a large-scale database

Document Type

Article

Publication Date

12-1-2007

Abstract

This paper first discusses some theoretical properties of 2D principal component analysis (2DPCA) and then presents a horizontal and vertical 2DPCA-based discriminant analysis (HVDA) method for face verification. The HVDA method, which applies 2DPCA horizontally and vertically on the image matrices (2D arrays), achieves lower computational complexity than the traditional PCA and Fisher linear discriminant analysis (LDA)-based methods that operate on high dimensional image vectors (ID arrays). The horizontal 2DPCA is invariant to vertical image translations and vertical mirror imaging, and the vertical 2DPCA is invariant to horizontal image translations and horizontal mirror imaging. The HVDA method is therefore less sensitive to imprecise eye detection and face cropping, and can improve upon the traditional discriminant analysis methods for face verification. Experiments using the face recognition grand challenge (FRGC) and the biometrie experimentation environment system show the effectiveness of the proposed method. In particular, for the most challenging FRGC version 2 Experiment 4, which contains 12 776 training images, 16 028 controlled target images, and 8014 uncontrolled query images, the HVDA method using a color configuration across two color spaces, namely, the Y IQ and the Y CbCr color spaces, achieves the face verification rate (ROC III) of 78.24 % at the false accept rate of 0.1 %. © 2007 IEEE.

Identifier

36349002960 (Scopus)

Publication Title

IEEE Transactions on Information Forensics and Security

External Full Text Location

https://doi.org/10.1109/TIFS.2007.910239

ISSN

15566013

First Page

781

Last Page

792

Issue

4

Volume

2

Grant

2006AA01Z119

Fund Ref

U.S. Department of Justice

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