Shallow CNNs for the Reliable Detection of Facial Marks
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.
- Citation
- BibTeX
Zeinstra, C. & Haasnoot, E.,
(2018).
Shallow CNNs for the Reliable Detection of Facial Marks.
In:
Brömme, A., Busch, C., Dantcheva, A., Rathgeb, C. & Uhl, A.
(Hrsg.),
BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group.
Bonn:
Köllen Druck+Verlag GmbH.
@inproceedings{mci/Zeinstra2018,
author = {Zeinstra, Chris AND Haasnoot, Erwin},
title = {Shallow CNNs for the Reliable Detection of Facial Marks},
booktitle = {BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group},
year = {2018},
editor = {Brömme, Arslan AND Busch, Christoph AND Dantcheva, Antitza AND Rathgeb, Christian AND Uhl, Andreas},
publisher = {Köllen Druck+Verlag GmbH},
address = {Bonn}
}
author = {Zeinstra, Chris AND Haasnoot, Erwin},
title = {Shallow CNNs for the Reliable Detection of Facial Marks},
booktitle = {BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group},
year = {2018},
editor = {Brömme, Arslan AND Busch, Christoph AND Dantcheva, Antitza AND Rathgeb, Christian AND Uhl, Andreas},
publisher = {Köllen Druck+Verlag GmbH},
address = {Bonn}
}
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More Info
ISBN: 978-3-88579-676-4
ISSN: 1617-5468
xmlui.MetaDataDisplay.field.date: 2018
Language:
(en)

Content Type: Text/Conference Paper