View source for Publications:Periocular Recognition Using CNN Features Off-the-Shelf

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{{PublicationSetupTemplate|Author=Kevin Hernandez-Diaz, Fernando Alonso-Fernandez, Josef Bigun
|PID=1238869
|Name=Hernandez-Diaz, Kevin (kevher) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Alonso-Fernandez, Fernando (feralo) (0000-0002-1400-346X) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Bigun, Josef (josef) (0000-0002-4929-1262) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650))
|Title=Periocular Recognition Using CNN Features Off-the-Shelf
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
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|Conference=International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, Sept. 26-29, 2018
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|Year=2018
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|Urls=https://arxiv.org/abs/1809.06157
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|NBN=urn:nbn:se:hh:diva-37704
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|Keywords=Periocular recognition;deep learning;biometrics;Convolutional Neural Network
|Categories=Signalbehandling (20205)
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|Projects=SIDUS-AIR
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|Abstract=<p>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 offthe-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.</p>
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|CreatedDate=2018-08-14
|PublicationDate=2018-08-14
|LastUpdated=2018-10-02
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1238869}}
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