Parallel adaboost algorithm for gabor wavelet selection in face recognition

Ulaş Baǧci; Li Bai

doi:10.1109/ICIP.2008.4712086

Parallel adaboost algorithm for gabor wavelet selection in face recognition

Ulaş Baǧci^*, Li Bai

^*Corresponding author for this work

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

4 Scopus citations

Abstract

In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on efficiency. Effective image features are selected by using properly chosen Gabor wavelets optimised with Parallel AdaBoost method and mutual information to get high recognition rates with low computational cost. Experiments are conducted using the well-known FERET face database. In proposed framework, memory and computation costs are reduced significantly and high classification accuracy is obtained.

Original language	English (US)
Title of host publication	2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages	1640-1643
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2008.4712086
State	Published - 2008
Externally published	Yes
Event	2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States Duration: Oct 12 2008 → Oct 15 2008

Publication series

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

Other

Other	2008 IEEE International Conference on Image Processing, ICIP 2008
Country	United States
City	San Diego, CA
Period	10/12/08 → 10/15/08

Keywords

Classification
Face recognition
Feature selection
Gabor wavelets
Parallel AdaBoost

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

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title = "Parallel adaboost algorithm for gabor wavelet selection in face recognition",

abstract = "In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on efficiency. Effective image features are selected by using properly chosen Gabor wavelets optimised with Parallel AdaBoost method and mutual information to get high recognition rates with low computational cost. Experiments are conducted using the well-known FERET face database. In proposed framework, memory and computation costs are reduced significantly and high classification accuracy is obtained.",

keywords = "Classification, Face recognition, Feature selection, Gabor wavelets, Parallel AdaBoost",

author = "Ula{\c s} Baǧci and Li Bai",

year = "2008",

doi = "10.1109/ICIP.2008.4712086",

language = "English (US)",

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

pages = "1640--1643",

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Baǧci, U & Bai, L 2008, Parallel adaboost algorithm for gabor wavelet selection in face recognition. in 2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings., 4712086, Proceedings - International Conference on Image Processing, ICIP, pp. 1640-1643, 2008 IEEE International Conference on Image Processing, ICIP 2008, San Diego, CA, United States, 10/12/08. https://doi.org/10.1109/ICIP.2008.4712086

TY - GEN

T1 - Parallel adaboost algorithm for gabor wavelet selection in face recognition

AU - Baǧci, Ulaş

AU - Bai, Li

PY - 2008

Y1 - 2008

N2 - In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on efficiency. Effective image features are selected by using properly chosen Gabor wavelets optimised with Parallel AdaBoost method and mutual information to get high recognition rates with low computational cost. Experiments are conducted using the well-known FERET face database. In proposed framework, memory and computation costs are reduced significantly and high classification accuracy is obtained.

AB - In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on efficiency. Effective image features are selected by using properly chosen Gabor wavelets optimised with Parallel AdaBoost method and mutual information to get high recognition rates with low computational cost. Experiments are conducted using the well-known FERET face database. In proposed framework, memory and computation costs are reduced significantly and high classification accuracy is obtained.

KW - Classification

KW - Face recognition

KW - Feature selection

KW - Gabor wavelets

KW - Parallel AdaBoost

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M3 - Conference contribution

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SN - 9781424417643

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 1640

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BT - 2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings

T2 - 2008 IEEE International Conference on Image Processing, ICIP 2008

Y2 - 12 October 2008 through 15 October 2008

ER -

Parallel adaboost algorithm for gabor wavelet selection in face recognition

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Keywords

ASJC Scopus subject areas

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