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Low Level Multispectral Palmprint Image Fusion for Large Scale Biometrics Authentication
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Author(s): Dakshina Ranjan Kisku (Asansol Engineering College, India), Phalguni Gupta (Indian Institute of Technology Kanpur, India), Jamuna Kanta Sing (Jadavpur University, India), Massimo Tistarelli (University of Sassari, Italy)and C. Jinsong Hwang (Texas State University, USA)
Copyright: 2012
Pages: 16
Source title:
Continuous Authentication Using Biometrics: Data, Models, and Metrics
Source Author(s)/Editor(s): Issa Traore (University of Victoria, Canada)and Ahmed Awad E. Ahmed (University of Victoria, Canada)
DOI: 10.4018/978-1-61350-129-0.ch005
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Abstract
Continuous biometric authentication is a process where the installed biometric systems continuously monitor and authenticate the users. Biometric system could be an exciting application to log in to computers and in a network system. However, due to malfunctioning in high-security zones, it is necessary to prevent those loopholes that often occur in security zones. It has been seen that when a user is logged in to such systems by authenticating to the biometric system installed, he/she often takes short breaks. In the meantime some imposter may attack the network or access to the computer system until the real user is logged out. Therefore, it is necessary to monitor the log in process of the system or network by continuous authentication of users. To accomplish this work we propose in this chapter a continuous biometric authentication system using low level fusion of multispectral palm images where the fusion is performed using wavelet transformation and decomposition. Fusion of palmprint instances is performed by wavelet transform and decomposition. To capture the palm characteristics, a fused image is convolved with Gabor wavelet transform. The Gabor wavelet feature representation reflects very high dimensional space. To reduce the high dimensionality, ant colony optimization algorithm is applied to select relevant, distinctive, and reduced feature set from Gabor responses. Finally, the reduced set of features is trained with support vector machines and accomplishes user recognition tasks. For evaluation, CASIA multispectral palmprint database is used. The experimental results reveal that the system is found to be robust and encouraging while variations of classifiers are used. Also a comparative study of the proposed system with a well-known method is presented.
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