NDLI: Gaussian Shape Descriptor for Palmprint Authentication

Content Provider	Springer Nature Link
Author	Zheng, Ping
Copyright Year	2010
Abstract	Single-modal biometrics has certain inherent problems such as noisy sensor data, non-universality of biometric traits, restricted degrees of freedom and the stability of the single algorithm itself. In order to overcome these problems and obtain a satisfactory recognition rate while maintaining high robustness, an effective solution is to employ multi-modal biometrics. Specifically, single-modal biometric multiple representation fusion is a form of multi-modal biometrics, which involves using multiple representations on a single biometric indicator. Moreover, multiple representation fusion is actually feature fusion. The key to feature fusion is how to deal with the local uncertainty. Drawing lessons from the human cognitive process, manifold, is introduced in order to realize a smooth transition from local to global on the basis of topology. In this paper, we present a novel scheme for fusing the Palmprint Mixed-Phase Features and the Palmprint Directional Valley Features, termed Shape of Gaussian (SOG) matching, which yields equivalent results to the feature fusion approaches employed in previous work. Furthermore, since SOGs form a Lie group, and Lie group is an important kind of manifold, a distance metric for SOG based on Lie group theory is adopted. Experimental results illustrate the effectiveness of the proposed approach.
Starting Page	303
Ending Page	311
Page Count	9
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	2
Issue Number	4
e-ISSN	18669964
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-07-13
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-modal biometrics PMPF and PDVF Shape of Gaussian (SOG) Lie group theory Computational Biology/Bioinformatics Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Neurosciences
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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