Face Recognition Based on Gabor Features

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Abstract

The paper presents a novel approach for solving face recognition problem. We combine Gabor filters and Principal Component Analysis (PCA) to extract feature vectors; then we apply Support Vector Machine (SVM), the most powerful discriminative method, and AdaBoost, a meta-algorithm, for classification. Experiments for the proposed method have been conducted on two public face database AT&T and FERET. The results show that the proposed method could improve the classification rates
Original languageEnglish
Title of host publicationVisual Information Processing EUVIP 2011, 3rd European Workshop
EditorsAzeddine Beghdadi, Abdesselam Bouzerdoum, Giuseppe Boccignone, Mohamed-Chaker Larabi
Place of PublicationParis
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages264-269
Number of pages6
Volume1
ISBN (Print)9781457700729
DOIs
Publication statusPublished - 2011
EventEuropean Workshop on Visual Information Processing - Paris, Paris, France
Duration: 4 Jul 20116 Jul 2011

Conference

ConferenceEuropean Workshop on Visual Information Processing
CountryFrance
CityParis
Period4/07/116/07/11

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Cite this

Tran, D., Huang, X., & Chetty, G. (2011). Face Recognition Based on Gabor Features. In A. Beghdadi, A. Bouzerdoum, G. Boccignone, & M-C. Larabi (Eds.), Visual Information Processing EUVIP 2011, 3rd European Workshop (Vol. 1, pp. 264-269). Paris: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EuVIP.2011.6045542