Facial recognition/verification using Gabor wavelets and Kernel methods

Linlin Shen; Li Bai; Phil Picton

doi:10.1109/ICIP.2004.1421332

Facial recognition/verification using Gabor wavelets and Kernel methods

Linlin Shen, Li Bai, Phil Picton

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

A novel Gabor-Kernel face classification method is proposed in this paper. This involves convolving a face image with a series of Gabor kernels at different scales, locations, and orientations to obtain feature vectors. Kernel methods such as Kernel Principal Component Analysis (KPCA) and Kernel Discriminant Analysis (KDA) are then applied to the feature vectors for dimension reduction as well as class separability enhancement. The method has been applied to both face recognition and verification for performance evaluation. Two standard databases: FERET and BANCA database are used for testing. Both results show the robustness of the method: Gabor + KDA against the variance of expression, illumination and pose.

Original language	English
Title of host publication	2004 International Conference on Image Processing, ICIP 2004
Pages	1433-1436
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2004.1421332
Publication status	Published - 2004
Externally published	Yes
Event	2004 International Conference on Image Processing, ICIP 2004 - , Singapore Duration: 18 Oct 2004 → 21 Oct 2004

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	3
ISSN (Print)	1522-4880

Conference

Conference	2004 International Conference on Image Processing, ICIP 2004
Country/Territory	Singapore
Period	18/10/04 → 21/10/04

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/ICIP.2004.1421332

Cite this

@inproceedings{d40d6d4f4c1d4f57bc5f90fb8959cf1f,

title = "Facial recognition/verification using Gabor wavelets and Kernel methods",

abstract = "A novel Gabor-Kernel face classification method is proposed in this paper. This involves convolving a face image with a series of Gabor kernels at different scales, locations, and orientations to obtain feature vectors. Kernel methods such as Kernel Principal Component Analysis (KPCA) and Kernel Discriminant Analysis (KDA) are then applied to the feature vectors for dimension reduction as well as class separability enhancement. The method has been applied to both face recognition and verification for performance evaluation. Two standard databases: FERET and BANCA database are used for testing. Both results show the robustness of the method: Gabor + KDA against the variance of expression, illumination and pose.",

author = "Linlin Shen and Li Bai and Phil Picton",

year = "2004",

doi = "10.1109/ICIP.2004.1421332",

language = "English",

isbn = "0780385543",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "1433--1436",

booktitle = "2004 International Conference on Image Processing, ICIP 2004",

note = "2004 International Conference on Image Processing, ICIP 2004 ; Conference date: 18-10-2004 Through 21-10-2004",

}

Shen, L, Bai, L & Picton, P 2004, Facial recognition/verification using Gabor wavelets and Kernel methods. in 2004 International Conference on Image Processing, ICIP 2004. Proceedings - International Conference on Image Processing, ICIP, vol. 3, pp. 1433-1436, 2004 International Conference on Image Processing, ICIP 2004, Singapore, 18/10/04. https://doi.org/10.1109/ICIP.2004.1421332

TY - GEN

T1 - Facial recognition/verification using Gabor wavelets and Kernel methods

AU - Shen, Linlin

AU - Bai, Li

AU - Picton, Phil

PY - 2004

Y1 - 2004

N2 - A novel Gabor-Kernel face classification method is proposed in this paper. This involves convolving a face image with a series of Gabor kernels at different scales, locations, and orientations to obtain feature vectors. Kernel methods such as Kernel Principal Component Analysis (KPCA) and Kernel Discriminant Analysis (KDA) are then applied to the feature vectors for dimension reduction as well as class separability enhancement. The method has been applied to both face recognition and verification for performance evaluation. Two standard databases: FERET and BANCA database are used for testing. Both results show the robustness of the method: Gabor + KDA against the variance of expression, illumination and pose.

AB - A novel Gabor-Kernel face classification method is proposed in this paper. This involves convolving a face image with a series of Gabor kernels at different scales, locations, and orientations to obtain feature vectors. Kernel methods such as Kernel Principal Component Analysis (KPCA) and Kernel Discriminant Analysis (KDA) are then applied to the feature vectors for dimension reduction as well as class separability enhancement. The method has been applied to both face recognition and verification for performance evaluation. Two standard databases: FERET and BANCA database are used for testing. Both results show the robustness of the method: Gabor + KDA against the variance of expression, illumination and pose.

UR - http://www.scopus.com/inward/record.url?scp=20444452087&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2004.1421332

DO - 10.1109/ICIP.2004.1421332

M3 - Conference contribution

AN - SCOPUS:20444452087

SN - 0780385543

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 1433

EP - 1436

BT - 2004 International Conference on Image Processing, ICIP 2004

T2 - 2004 International Conference on Image Processing, ICIP 2004

Y2 - 18 October 2004 through 21 October 2004

ER -

Facial recognition/verification using Gabor wavelets and Kernel methods

Abstract

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