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

2017 | OriginalPaper | Chapter

Efficient Multimodal Biometric Feature Fusion Using Block Sum and Minutiae Techniques

Authors : Ujwalla Gawande, Kamal Hajari, Yogesh Golhar

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

Publisher: Springer Singapore

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Abstract

Biometric is widely used for identifying a person in different area like Security zones, Border crossings, Airports, Automatic teller machines, Passport, Criminal verification, etc. Currently, most of the deployed biometric systems use a single biometric trait for recognition. But there are several limitations of unimodal biometric system, such as Noise in sensed data, Non-universality, higher error rate, and lower recognition rate. These issues can be handled by designing a Multimodal biometric system. This research paper proposes a novel feature level fusion technique based on a distance metric to improve both recognition rate and response time. This algorithm is based on the textural features extracted from iris using Block sum and fingerprint using Minutiae method. The performance of the propose algorithms has been validated and compared with the other algorithms using the CASIA Version 3 iris database and YCCE Fingerprint database.

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previous chapter Separating Indic Scripts with ‘matra’—A Precursor to Script Identification in Multi-script Documents
next chapter Video Synopsis for IR Imagery Considering Video as a 3D Data Cuboid
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Metadata
Title
Efficient Multimodal Biometric Feature Fusion Using Block Sum and Minutiae Techniques
Authors
Ujwalla Gawande
Kamal Hajari
Yogesh Golhar
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_20