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Periocular Recognition Using CNN Features Off-the-Shelf
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Datum
2018
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Verlag
Köllen Druck+Verlag GmbH
Zusammenfassung
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 off-the-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.