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Background Modeling Using Temporal-Local Sample Density Outlier Detection Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Design of Advanced Correlation Filters for Finger Knuckle Print Authentication Systems

Authors : Gaurav Verma, Aloka Sinha

Published in: Proceedings of International Conference on Computer Vision and Image Processing

Publisher: Springer Singapore

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Abstract

Biometric recognition systems use automated processes in which the stored templates are used to match with the live biometrics traits. Correlation filter is a promising technique for such type of matching in which the correlation output is obtained at the location of the target in the correlation plane. Among all kinds of available biometrics, finger knuckle print (FKP) has usable significant feature, which can be utilized for person identification and verification. Motivated by the utility of the FKP, this paper presents a new scheme for person authentication using FKP based on advanced correlation filters (ACF). The numerical experiments have been carried out on the Poly U FKP database to evaluate the performance of the designed filters. The obtained results demonstrate the effectiveness and show better discrimination ability between the genuine and the imposter population for the proposed scheme.

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previous chapter Finger Knuckle Print Recognition Based on Wavelet and Gabor Filtering
next chapter A Nonlinear Modified CONVEF-AD Based Approach for Low-Dose Sinogram Restoration
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Metadata
Title
Design of Advanced Correlation Filters for Finger Knuckle Print Authentication Systems
Authors
Gaurav Verma
Aloka Sinha
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of International Conference on Computer Vision and Image Processing

Print ISBN: 978-981-10-2103-9

Electronic ISBN: 978-981-10-2104-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-2104-6_5