NDLI: Face Recognition using Bank of Gabor Filters

Content Provider	IEEE Xplore Digital Library
Author	Mohsin, S.M. Javed, M.Y. Anjum, A.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Nat. Univ. of Sci. & Technol., Rawalpindi (Mohsin, S.M.)
Abstract	The biometric schemes are commonly used for the identification of human beings. Face recognition approach has been employed using different algorithms for the purpose of identification of individuals. This paper describes utilization of Gabor filters for selecting feature vectors/coefficients. It explains construction of Gabor filters, selection of peaks, feature storage and classification of faces. Standard and improved classifier schemes have been used to evaluate the developed face recognition system in terms of detection rate and speed by using 20 to 60 Gabor filters. Evaluation results have been obtained through ORL database where 200 images have been used for training and 200 images for testing (i.e. first five images of each person for training and remaining five images for testing). The results show that the improved classifier extracts 8878 feature vectors for a bank of 40 Gabor filters and provides detection rate of 92.5 % whereas a bank of 30 Gabor filters provided detection rate of 93% by extracting 8700 feature vectors. It has also been noted that training and time for a single image using 30 Gabor filters is 5.35 and 14.5 seconds respectively which is less as compared to 40 Gabor filter-based system
Starting Page	144
Ending Page	150
File Size	160665
Page Count	7
File Format	PDF
ISBN	1424405025
DOI	10.1109/ICET.2006.336021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-13
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biometrics Image databases Face recognition Humans Feature extraction Spatial databases Gabor filters Face detection Standards development Testing
Content Type	Text
Resource Type	Article

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