Three-dimensional face recognition: An Eigensurface approach

T Heseltine; N Pears; J Austin

doi:10.1109/ICIP.2004.1419769

Three-dimensional face recognition: An Eigensurface approach

T Heseltine, N Pears, J Austin

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching.

Original language	English
Title of host publication	ICIP: 2004 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1- 5
Place of Publication	NEW YORK
Publisher	IEEE
Pages	1421-1424
Number of pages	4
Volume	2
ISBN (Print)	0-7803-8554-3
DOIs	https://doi.org/10.1109/ICIP.2004.1419769
Publication status	Published - 2004
Event	International Conference on Image Processing (ICIP 2004) - Singapore Duration: 24 Oct 2004 → 27 Oct 2004

Conference

Conference	International Conference on Image Processing (ICIP 2004)
City	Singapore
Period	24/10/04 → 27/10/04

Bibliographical note

© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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10.1109/ICIP.2004.1419769

austinj4.pdf

http://dx.doi.org/10.1109/ICIP.2004.1419769

Cite this

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title = "Three-dimensional face recognition: An Eigensurface approach",

abstract = "We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching.",

author = "T Heseltine and N Pears and J Austin",

note = "{\textcopyright} 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; International Conference on Image Processing (ICIP 2004) ; Conference date: 24-10-2004 Through 27-10-2004",

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doi = "10.1109/ICIP.2004.1419769",

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AU - Heseltine, T

AU - Pears, N

AU - Austin, J

N1 - © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2004

Y1 - 2004

N2 - We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching.

AB - We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching.

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