Konferenzbeitrag
Shallow CNNs for the Reliable Detection of Facial Marks
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Datum
2018
Autor:innen
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Köllen Druck+Verlag GmbH
Zusammenfassung
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.