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Periocular Recognition Using CNN Features Off-the-Shelf

Author:
Hernandez-Diaz, Kevin [DBLP] ;
Alonso-Fernandez, Fernando [DBLP] ;
Bigun, Josef [DBLP]
Abstract
Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these off-the-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to 40%, with the fusion of CNN and traditional features providing additional improvements.
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Hernandez-Diaz, K., Alonso-Fernandez, F. & Bigun, J., (2018). Periocular Recognition Using CNN Features Off-the-Shelf. 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/Hernandez-Diaz2018,
author = {Hernandez-Diaz, Kevin AND Alonso-Fernandez, Fernando AND Bigun, Josef},
title = {Periocular Recognition Using CNN Features Off-the-Shelf},
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: 1617-5469
xmlui.MetaDataDisplay.field.date: 2018
Language: en (en)
Content Type: Text/Conference Paper

Keywords

  • Periocular recognition
  • deep learning
  • biometrics
  • Convolutional Neural Network.
Collections
  • P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group [32]

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Diese Digital Library basiert auf DSpace.