Publications:Cross-Spectral Biometric Recognition with Pretrained CNNs as Generic Feature Extractors

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Title Cross-Spectral Biometric Recognition with Pretrained CNNs as Generic Feature Extractors
Author Kevin Hernandez-Diaz and Fernando Alonso-Fernandez and Josef Bigun
Year 2019
PublicationType Conference Paper
Journal
HostPublication
Conference Swedish Symposium on Image Analysis, SSBA, Gothenburg, Swede), March 19-20, 2019
DOI
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1353941
Abstract Periocular recognition has gained attention in the last years thanks to its high discrimination capabilities in less constraint scenarios than face or iris. In this paper we propose a method for periocular verification under different light spectra using CNN features with the particularity that the network has not been trained for this purpose. We use a ResNet-101 pretrained model for the ImageNet Large Scale Visual Recognition Challenge to extract features from the IIITD Multispectral Periocular Database. At each layer the features are compared using χ 2 distance and cosine similitude to carry on verification between images, achieving an improvement in the EER and accuracy at 1% FAR of up to 63.13% and 24.79% in comparison to previous works that employ the same database. In addition to this, we train a neural network to match the best CNN feature layer vector from each spectrum. With this procedure, we achieve improvements of up to 65% (EER) and 87% (accuracy at 1% FAR) in cross-spectral verification with respect to previous studies.