]> 2026-04-04T16:48:18+02:00 Publications:Periocular Recognition Using CNN Features Off-the-Shelf 500 en <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> International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, Sept. 26-29, 2018 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1238869 Conference Paper Periocular Recognition Using CNN Features Off-the-Shelf 2018 2018-10-02T20:21:52Z 2458394.3485185 Periocular Recognition Using CNN Features Off-the-Shelf 500 0 plainlist list 1 [[:Publications:Periocular Recognition Using CNN Features Off-the-Shelf]] Periocular Recognition Using CNN Features Off-the-Shelf# QUERY9db1a8eabed541e805cc433cc8a30072 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Periocular Recognition Using CNN Features Off-the-Shelf# ERRe24f17e090c98448f3de99d03182b7b6