• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Deep and multi-algorithmic gender classification of single fingerprint minutiae
 
  • Details
  • Full
Options
2018
Conference Paper
Title

Deep and multi-algorithmic gender classification of single fingerprint minutiae

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.
Author(s)
Terhörst, Philipp  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Braun, Andreas
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
21st International Conference on Information Fusion, FUSION 2018  
Project(s)
CRISP
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
International Conference on Information Fusion (FUSION) 2018  
DOI
10.23919/ICIF.2018.8455803
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Digitized Work

  • Lead Topic: Visual Computing as a Service

  • biometric

  • fingerprint recognition

  • CRISP

  • feature extraction

  • Feature Classification

  • Research Line: Computer vision (CV)

  • Research Line: Human computer interaction (HCI)

  • ATHENE

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024