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Cross-Spectral Biometric Recognition with Pretrained CNNs as Generic Feature Extractors
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-1400-346X
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-4929-1262
2019 (engelsk)Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

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.

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2019.
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URN: urn:nbn:se:hh:diva-40625OAI: oai:DiVA.org:hh-40625DiVA, id: diva2:1353941
Konferanse
Swedish Symposium on Image Analysis, SSBA, Gothenburg, Sweden, March 19-20, 2019
Tilgjengelig fra: 2019-09-24 Laget: 2019-09-24 Sist oppdatert: 2019-10-11

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Hernandez-Diaz, KevinAlonso-Fernandez, FernandoBigun, Josef

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