Nonsingular discriminant feature extraction for face recognition

Chih P. Liao*, Jen-Tzung Chien

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

It is popular to extract discriminant features using Fisher linear discriminant analysis (LDA) for general pattern recognition. LDA aims to find an optimal discriminant transformation matrix, which maximizes the ratio of between-class scatter to within-class scatter. However, in case of small sample size and high dimensional data, LDA is prone to be unrealizable due to the singularity of scatter matrices. In this paper, we present a nonsingular transformation prior to performing LDA. This method is to transform general features using all eigenvectors of scatter matrix with nonzero eigenvalues. As a result, the scatter matrix of transformed features is nonsingular. Subsequently, the discriminant transformation is applied according to LDA using the new scatter matrices. The superiority of nonsingular discriminant analysis of between-class matrix comes from the shrinkage of within-class scatters and accordingly the enhancement of Fisher class separability. From the experiments on facial databases, we find that the nonsingular discriminant feature extraction achieves significant face recognition performance compared to other LDA-related methods for a wide range of sample sizes and class numbers.

Original languageEnglish
Title of host publication2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
DOIs
StatePublished - 1 Dec 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: 18 Mar 200523 Mar 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeII
ISSN (Print)1520-6149

Conference

Conference2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period18/03/0523/03/05

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  • Cite this

    Liao, C. P., & Chien, J-T. (2005). Nonsingular discriminant feature extraction for face recognition. In 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing [1415565] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. II). https://doi.org/10.1109/ICASSP.2005.1415565