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Minutiae-Based Gender Estimation for Full and Partial Fingerprints of Arbitrary Size and Shape

Terhörst, Philipp and Damer, Naser and Braun, Andreas and Kuijper, Arjan (2019):
Minutiae-Based Gender Estimation for Full and Partial Fingerprints of Arbitrary Size and Shape.
In: Computer Vision – ACCV 2018, Cham, Springer, In: Asian Conference on Computer Vision (ACCV), Perth, Australia, 2018, In: Lecture Notes in Computer Science (LNCS), 11361, ISSN 0302-9743,
ISBN 978-3-030-20886-8,
DOI: 10.1007/978-3-030-20887-5_11,
[Online-Edition: https://doi.org/10.1007/978-3-030-20887-5_11],
[Conference or Workshop Item]

Abstract

Since fingerprints are one of the most widely deployed biometrics, accurate fingerprint gender estimation can positively affect several applications. For example, in criminal investigations, gender classification may significantly minimize the list of potential subjects. Previous work mainly offered solutions for the task of gender classification based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications, including forensics and the fast growing field of consumer electronics. Due to its huge variability in size and shape, gender estimation on partial fingerprints is a challenging problem. Therefore, in this work we propose a flexible gender estimation scheme by building a gender classifier based on an ensemble of minutiae. The outputs of the single minutia gender predictions are combined by a novel adjusted score fusion approach to obtain an enhanced gender decision. Unlike classical solutions this allows to deal with unconstrained fingerprint parts of arbitrary size and shape. We performed investigations on a publicly available database and our proposed solution proved to significantly outperform state-of-the-art approaches on both full and partial fingerprints. The experiments indicate a reduction in the gender estimation error by 19.34% on full fingerprints and 28.33% on partial captures in comparison to previous work.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Terhörst, Philipp and Damer, Naser and Braun, Andreas and Kuijper, Arjan
Title: Minutiae-Based Gender Estimation for Full and Partial Fingerprints of Arbitrary Size and Shape
Language: English
Abstract:

Since fingerprints are one of the most widely deployed biometrics, accurate fingerprint gender estimation can positively affect several applications. For example, in criminal investigations, gender classification may significantly minimize the list of potential subjects. Previous work mainly offered solutions for the task of gender classification based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications, including forensics and the fast growing field of consumer electronics. Due to its huge variability in size and shape, gender estimation on partial fingerprints is a challenging problem. Therefore, in this work we propose a flexible gender estimation scheme by building a gender classifier based on an ensemble of minutiae. The outputs of the single minutia gender predictions are combined by a novel adjusted score fusion approach to obtain an enhanced gender decision. Unlike classical solutions this allows to deal with unconstrained fingerprint parts of arbitrary size and shape. We performed investigations on a publicly available database and our proposed solution proved to significantly outperform state-of-the-art approaches on both full and partial fingerprints. The experiments indicate a reduction in the gender estimation error by 19.34% on full fingerprints and 28.33% on partial captures in comparison to previous work.

Title of Book: Computer Vision – ACCV 2018
Series Name: Lecture Notes in Computer Science (LNCS)
Volume: 11361
Place of Publication: Cham
Publisher: Springer
ISBN: 978-3-030-20886-8
Uncontrolled Keywords: Biometrics, Classifications, Face recognition
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: Asian Conference on Computer Vision (ACCV)
Event Location: Perth, Australia
Event Dates: 2018
Date Deposited: 01 Jul 2019 08:46
DOI: 10.1007/978-3-030-20887-5_11
Official URL: https://doi.org/10.1007/978-3-030-20887-5_11
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