Three-Dimensional Face Recognition:  A Fishersurface Approach

Thomas Heseltine; Nick Pears; Jim Austin

Three-Dimensional Face Recognition: A Fishersurface Approach

Thomas Heseltine, Nick Pears, Jim Austin

Computer Science

Research output: Contribution to conference › Paper › peer-review

Abstract

Previous work has shown that principal component analysis (PCA) of three-dimensional face models can be used to perform recognition to a high degree of accuracy. However, experimentation with two-dimensional face images has shown that PCA-based systems are improved by incorporating linear discriminant analysis (LDA), as with Belhumier et al’s fisherface approach. In this paper we introduce the fishersurface method of face recognition: an adaptation of the two-dimensional fisherface approach to three-dimensional facial surface data. Testing a variety of pre-processing techniques, we identify the most effective facial surface representation and distance metric for use in such application areas as security, surveillance and data compression. Results are presented in the form of false acceptance and false rejection rates, taking the equal error rate as a single comparative value.

Original language	Undefined/Unknown
Pages	684-691
Publication status	Published - 2004

Access to Document

http://springerlink.metapress.com/openurl.asp?genre=articleissn=0302-9743volume=3212spage=684

Cite this

@conference{46f5555ea27544ebb64403fd6bf5da73,

title = "Three-Dimensional Face Recognition: A Fishersurface Approach",

abstract = "Previous work has shown that principal component analysis (PCA) of three-dimensional face models can be used to perform recognition to a high degree of accuracy. However, experimentation with two-dimensional face images has shown that PCA-based systems are improved by incorporating linear discriminant analysis (LDA), as with Belhumier et al{\textquoteright}s fisherface approach. In this paper we introduce the fishersurface method of face recognition: an adaptation of the two-dimensional fisherface approach to three-dimensional facial surface data. Testing a variety of pre-processing techniques, we identify the most effective facial surface representation and distance metric for use in such application areas as security, surveillance and data compression. Results are presented in the form of false acceptance and false rejection rates, taking the equal error rate as a single comparative value.",

author = "Thomas Heseltine and Nick Pears and Jim Austin",

year = "2004",

language = "Undefined/Unknown",

pages = "684--691",

}

TY - CONF

T1 - Three-Dimensional Face Recognition

T2 - A Fishersurface Approach

AU - Heseltine, Thomas

AU - Pears, Nick

AU - Austin, Jim

PY - 2004

Y1 - 2004

N2 - Previous work has shown that principal component analysis (PCA) of three-dimensional face models can be used to perform recognition to a high degree of accuracy. However, experimentation with two-dimensional face images has shown that PCA-based systems are improved by incorporating linear discriminant analysis (LDA), as with Belhumier et al’s fisherface approach. In this paper we introduce the fishersurface method of face recognition: an adaptation of the two-dimensional fisherface approach to three-dimensional facial surface data. Testing a variety of pre-processing techniques, we identify the most effective facial surface representation and distance metric for use in such application areas as security, surveillance and data compression. Results are presented in the form of false acceptance and false rejection rates, taking the equal error rate as a single comparative value.

AB - Previous work has shown that principal component analysis (PCA) of three-dimensional face models can be used to perform recognition to a high degree of accuracy. However, experimentation with two-dimensional face images has shown that PCA-based systems are improved by incorporating linear discriminant analysis (LDA), as with Belhumier et al’s fisherface approach. In this paper we introduce the fishersurface method of face recognition: an adaptation of the two-dimensional fisherface approach to three-dimensional facial surface data. Testing a variety of pre-processing techniques, we identify the most effective facial surface representation and distance metric for use in such application areas as security, surveillance and data compression. Results are presented in the form of false acceptance and false rejection rates, taking the equal error rate as a single comparative value.

M3 - Paper

SP - 684

EP - 691

ER -