Konferenzbeitrag
Unsupervised Learning of Fingerprint Rotations
Lade...
Volltext URI
Dokumententyp
Text/Conference Paper
Zusatzinformation
Datum
2018
Autor:innen
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Verlag
Köllen Druck+Verlag GmbH
Zusammenfassung
The alignment of fingerprint samples is a preprocessing step in fingerprint recognition.
It allows an improved biometric feature extraction and a more accurate biometric comparison. We
propose to use Convolutional Neural Networks for estimation of the rotational part. The main contribution
is an unsupervised training strategy similar to Siamese Networks for estimation of rotations.
The approach does not need any labelled data for training. It is trained to estimate orientation differences
for pairs of samples. Our approach achieves an alignment accuracy with a mean absolute
deviation 2:1 on data similar to the training data, which supports the alignment task. For other
datasets accuracies down to 6:2 mean absolute deviation are achieved.