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Deep and Multi-algorithmic Gender Classification of Single Fingerprint Minutiae

Terhörst, Philipp and Damer, Naser and Braun, Andreas and Kuijper, Arjan (2018):
Deep and Multi-algorithmic Gender Classification of Single Fingerprint Minutiae.
In: FUSION 2018, Los Alamitos, IEEE, In: International Conference on Information Fusion (FUSION), Cambridge, UK, 2018, ISBN 978-0-9964527-6-2,
[Conference or Workshop Item]

Abstract

Accurate fingerprint gender estimation can positively affect several applications, since fingerprints are one of the most widely deployed biometrics. For example, gender classification in criminal investigations 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. Moreover, partial fingerprints are not well-defined. Therefore, this work improves the gender decision performance on a well-defined partition of the fingerprint. It enhances gender estimation on the level of a single minutia. Working on this level, we propose three main contributions that were evaluated on a publicly available database. First, a convolutional neural network model is offered that outperformed baseline solutions based on hand crafted features. Second, several multi-algorithmic fusion approaches were tested by combining the outputs of different gender estimators that help further increase the classification accuracy. Third, we propose including minutia detection reliability in the fusion process, which leads to enhancing the total gender decision performance. The achieved gender classification performance of a single minutia is comparable to the accuracy that previous work reported on a quarter of aligned fingerprints including more than 25 minutiae.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Terhörst, Philipp and Damer, Naser and Braun, Andreas and Kuijper, Arjan
Title: Deep and Multi-algorithmic Gender Classification of Single Fingerprint Minutiae
Language: English
Abstract:

Accurate fingerprint gender estimation can positively affect several applications, since fingerprints are one of the most widely deployed biometrics. For example, gender classification in criminal investigations 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. Moreover, partial fingerprints are not well-defined. Therefore, this work improves the gender decision performance on a well-defined partition of the fingerprint. It enhances gender estimation on the level of a single minutia. Working on this level, we propose three main contributions that were evaluated on a publicly available database. First, a convolutional neural network model is offered that outperformed baseline solutions based on hand crafted features. Second, several multi-algorithmic fusion approaches were tested by combining the outputs of different gender estimators that help further increase the classification accuracy. Third, we propose including minutia detection reliability in the fusion process, which leads to enhancing the total gender decision performance. The achieved gender classification performance of a single minutia is comparable to the accuracy that previous work reported on a quarter of aligned fingerprints including more than 25 minutiae.

Title of Book: FUSION 2018
Place of Publication: Los Alamitos
Publisher: IEEE
ISBN: 978-0-9964527-6-2
Uncontrolled Keywords: Biometrics, Fingerprint recognition, Feature extraction, Feature classifications
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: International Conference on Information Fusion (FUSION)
Event Location: Cambridge, UK
Event Dates: 2018
Date Deposited: 17 Jul 2019 14:13
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