UM
A New Enhanced Nearest Feature Space (ENFS) Classifier for Gabor Wavelets Features-Based Face Recognition
Jianke Zhu; Mang I Vai; Peng Un Mak
2004
Conference Name1st International Conference on Biometric Authentication, ICBA 2004
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3072
Pages124-131
Conference Date7 15, 2004 - 7 17, 2004
Conference PlaceHung Hom, Hong kong
CountryCHINA
Author of SourceSpringer Verlag
Abstract

This paper proposes a new Enhanced Nearest Feature Space Classifier (ENFS) which inherits the generalization capability from Nearest Feature Space method. Additionally, estimated variance can optimize the class seperability in the sense of Bayes error, and has improve the classification power in reduced PCA subspace. Gabor wavelets representation of face images is an effective approach for both facial action recognition and face identification. Perform PCA dimensionality reduction on the downsampled Gabor Wavelets features can be effectively for face recognition. In our experiments, ENFS with proposed Gabor Wavelets Features shows very good performance, which can achieve 98.5% maximum correct recognition rate on ORL data set without any preprocessing step. 

DOIhttps://doi.org/10.1007/978-3-540-25948-0_18
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Imaging Science & Photographic Technology
WOS IDWOS:000222868800018
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversity of Macau, PO Box 3001, Macau, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jianke Zhu,Mang I Vai,Peng Un Mak. A New Enhanced Nearest Feature Space (ENFS) Classifier for Gabor Wavelets Features-Based Face Recognition[C]//Springer Verlag,2004:124-131.
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