Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Deep and multi-algorithmic gender classification of single fingerprint minutiae

 
: Terhörst, Philipp; Damer, Naser; Braun, Andreas; Kuijper, Arjan

:

Institute of Electrical and Electronics Engineers -IEEE-:
21st International Conference on Information Fusion, FUSION 2018 : 10-13 July 2018, Cambridge, United Kingdom
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-4330-3
ISBN: 978-0-9964527-6-2
ISBN: 978-0-9964527-7-9
S.2113-2120
International Conference on Information Fusion (FUSION) <21, 2018, Cambridge>
Bundesministerium für Bildung und Forschung BMBF
CRISP
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Guiding Theme: Digitized Work; Guiding Theme: Visual Computing as a Service; biometric; fingerprint recognition; CRISP; feature extraction; Feature Classification; Research Area: Computer vision (CV); Research Area: Human computer interaction (HCI)

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.

: http://publica.fraunhofer.de/dokumente/N-510078.html