Spectrum Translation for Cross-Spectral Ocular Matching
- URL: http://arxiv.org/abs/2002.06228v1
- Date: Fri, 14 Feb 2020 19:30:31 GMT
- Title: Spectrum Translation for Cross-Spectral Ocular Matching
- Authors: Kevin Hernandez Diaz, Fernando Alonso-Fernandez, Josef Bigun
- Abstract summary: Cross-spectral verification remains a big issue in biometrics, especially for the ocular area.
We investigate the use of Conditional Adversarial Networks for spectrum translation between near infra-red and visual light images for ocular biometrics.
- Score: 59.17685450892182
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cross-spectral verification remains a big issue in biometrics, especially for
the ocular area due to differences in the reflected features in the images
depending on the region and spectrum used.
In this paper, we investigate the use of Conditional Adversarial Networks for
spectrum translation between near infra-red and visual light images for ocular
biometrics. We analyze the transformation based on the overall visual quality
of the transformed images and the accuracy drop of the identification system
when trained with opposing data.
We use the PolyU database and propose two different systems for biometric
verification, the first one based on Siamese Networks trained with Softmax and
Cross-Entropy loss, and the second one a Triplet Loss network. We achieved an
EER of 1\% when using a Triplet Loss network trained for NIR and finding the
Euclidean distance between the real NIR images and the fake ones translated
from the visible spectrum. We also outperform previous results using baseline
algorithms.
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