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Gabor wavelets and General Discriminant Analysis for face identification and verification

Authors:

Michael FairhurstAuthors Info & Claims

Image and Vision Computing, Volume 25, Issue 5

Pages 553 - 563

https://doi.org/10.1016/j.imavis.2006.05.002

Published: 01 May 2007 Publication History

Abstract

A novel and uniform framework for both face identification and verification is presented in this paper. The framework is based on a combination of Gabor wavelets and General Discriminant Analysis, and can be considered appearance based in that features are extracted from the whole face image. The feature vectors are then subjected to subspace projection. The design of Gabor filters for facial feature extraction is also discussed, which is seldom reported in the literature. The method has been tested extensively for both identification and verification applications. The FERET and BANCA face databases were used to generate the results. Experiments show that Gabor wavelets can significantly improve system performance whilst General Discriminant Analysis outperforms other subspace projection methods such as Principal Component Analysis, Linear Discriminant Analysis, and Kernel Principal Component Analysis. Our method has achieved 97.5% recognition rate on the FERET database, and 5.96% verification error rate on the BANCA database. This is a significantly better performance than that attainable with other popular approaches reported in the literature. In particular, our verification system performed better than most of the systems in the 2004 International Face Verification Competition, using the BANCA face database and specially designed test protocols.

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Cited By

Hammouche RAttia AAkhrouf SAkhtar Z(2022)Gabor filter bank with deep autoencoder based face recognition systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116743197:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116743
Ghosh MKundu TGhosh DSarkar R(2022)Feature selection for facial emotion recognition using late hill-climbing based memetic algorithmMultimedia Tools and Applications10.1007/s11042-019-07811-x78:18(25753-25779)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-019-07811-x
Karn PHe XZhang JZhang Y(2020)An experimental study of relative total variation and probabilistic collaborative representation for iris recognitionMultimedia Tools and Applications10.1007/s11042-020-09553-779:43-44(31783-31801)Online publication date: 24-Aug-2020
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Index Terms

Gabor wavelets and General Discriminant Analysis for face identification and verification
1. Computing methodologies

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Information

Published In

cover image Image and Vision Computing

Image and Vision Computing Volume 25, Issue 5

May, 2007

245 pages

ISSN:0262-8856

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2006.

Publisher

Butterworth-Heinemann

United States

Publication History

Published: 01 May 2007

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Cited By

Hammouche RAttia AAkhrouf SAkhtar Z(2022)Gabor filter bank with deep autoencoder based face recognition systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116743197:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116743
Ghosh MKundu TGhosh DSarkar R(2022)Feature selection for facial emotion recognition using late hill-climbing based memetic algorithmMultimedia Tools and Applications10.1007/s11042-019-07811-x78:18(25753-25779)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-019-07811-x
Karn PHe XZhang JZhang Y(2020)An experimental study of relative total variation and probabilistic collaborative representation for iris recognitionMultimedia Tools and Applications10.1007/s11042-020-09553-779:43-44(31783-31801)Online publication date: 24-Aug-2020
https://dl.acm.org/doi/10.1007/s11042-020-09553-7
Liu SWang S(2018)An Improved Face Recognition Fusion Algorithm Based on the Features extracted from Gabor, PCA and KPCAProceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence10.1145/3206185.3206211(116-121)Online publication date: 24-Mar-2018
https://dl.acm.org/doi/10.1145/3206185.3206211
Kim JUm SMin D(2018)Fast 2D Complex Gabor Filter With Kernel DecompositionIEEE Transactions on Image Processing10.1109/TIP.2017.278362127:4(1713-1722)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1109/TIP.2017.2783621
Li LGe HTong YZhang Y(2018)Face Recognition Using Gabor-Based Feature Extraction and Feature Space Transformation Fusion Method for Single Image per Person ProblemNeural Processing Letters10.1007/s11063-017-9693-447:3(1197-1217)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11063-017-9693-4
Bedeloglu MTopcu ÇAkgul ADöger ESever ROzkan OOzkan OUysal HPolat OÇolak O(2018)Image-based Analysis of Emotional Facial Expressions in Full Face TransplantsJournal of Medical Systems10.1007/s10916-018-0895-842:3(1-10)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10916-018-0895-8
Saudi AKhalil MAbbas H(2018)Improving Audio-Visual Speech Recognition Using Gabor Recurrent Neural NetworksMultimodal Pattern Recognition of Social Signals in Human-Computer-Interaction10.1007/978-3-030-20984-1_7(71-83)Online publication date: 20-Aug-2018
https://dl.acm.org/doi/10.1007/978-3-030-20984-1_7
Chaudhary PSharma M(2017)VLSI Hardware Architecture of Real Time Pattern Classification using Naïve Bayes ClassifierProceedings of the 2017 2nd International Conference on Multimedia Systems and Signal Processing10.1145/3145511.3145521(61-65)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3145511.3145521
Sun ZHu ZWang MZhao S(2017)Discriminative feature learning‐based pixel difference representation for facial expression recognitionIET Computer Vision10.1049/iet-cvi.2016.050511:8(675-682)Online publication date: 20-Sep-2017
https://dl.acm.org/doi/10.1049/iet-cvi.2016.0505
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