Abstract
Facial marks are local irregularities of skin texture. Their type and/or spatial pattern can be used as a (soft) biometric modality in several applications. A key requirement for a biometric system that utilises facial marks is their reliable detection. Detection methods typically use a blob detector followed by heuristic post processing steps to reduce the number of false positives. In this paper, we consider shallow Convolutional Neural Networks (CNNs) for facial mark detection. The choice of this network type seems natural as it learns multiple (non) blob detectors; shallow refers to the fact that we only consider CNNs up to three layers. We show that (a) these CNNs successfully address the false positive problem, (b) remove the need for post processing steps, and (c) outperform a classic blob detector, approaches taken in previous studies and some other non CNN type classifiers in terms of EER and FMR at TMR=0.95.
Original language | English |
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Title of host publication | 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 |
Editors | Arslan Bromme, Andreas Uhl, Christoph Busch, Christian Rathgeb, Antitza Dantcheva |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
ISBN (Electronic) | 978-3-88579-676-3 |
ISBN (Print) | 978-1-5386-6007-2 |
DOIs | |
Publication status | Published - 10 Oct 2018 |
Event | 17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018 - Darmstadt, Germany Duration: 26 Sep 2018 → 28 Sep 2018 Conference number: 17 |
Publication series
Name | International Conference of the Biometrics Special Interest Group (BIOSIG) |
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Publisher | IEEE |
Volume | 2018 |
ISSN (Electronic) | 1617-5468 |
Conference
Conference | 17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018 |
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Abbreviated title | BIOSIG 2018 |
Country/Territory | Germany |
City | Darmstadt |
Period | 26/09/18 → 28/09/18 |
Keywords
- CNN
- Facial marks
- Forensics
- Image processing