NDLI: Multimodal biometric system using face, ear and gait biometrics

Content Provider	IEEE Xplore Digital Library
Author	Yazdanpanah, A.P. Faez, K. Amirfattahi, R.
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran (Faez, K.) \|\| Islamic Azad University of Najaf Abad, Isfahan, Iran (Yazdanpanah, A.P.) \|\| Digital Signal Processing Research Lab, Department of Electrical Engineering, Isfahan University of Technology, Iran (Amirfattahi, R.)
Abstract	In this paper, a novel multimodal biometric recognition system using three modalities including face, ear and gait, based on Gabor+PCA feature extraction method with fusion at matching score level is proposed. The performance of our approach has been studied under three different normalization methods (min-max, median- MAD and z-score) and two different fusion methods (weighted sum and weighted product). Our new method has been successfully tested using 360 images corresponding to 120 subjects from three databases including ORL face database, USTB ear database, and CASIA gait database. Because of these biometric traits, our proposed method requires no significant user cooperation and also can work from a long distance. According to the experimental results our proposed method exhibits excellent recognition performance and outperforms unimodal systems. The best recognition performance that our proposed method achieved is %97.5.
Starting Page	251
Ending Page	254
File Size	979465
Page Count	4
File Format	PDF
ISBN	9781424471652
e-ISBN	9781424471676
DOI	10.1109/ISSPA.2010.5605477
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-10
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ear Biometrics Face Biological system modeling Databases Conferences Gabor+PCA Multimodal Biometrics Face Biometric Ear Biometric Gait Biometric
Content Type	Text
Resource Type	Article

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