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Enhanced near-infrared periocular recognition through collaborative rendering of hand crafted and deep features

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<mark>Journal publication date</mark>31/03/2022
<mark>Journal</mark>Multimedia Tools and Applications
Issue number7
Number of pages15
Pages (from-to)9351–9365
Publication StatusPublished
Early online date18/01/22
<mark>Original language</mark>English


Periocular recognition leverage from larger feature region and lesser user cooperation, when compared against the traditional iris recognition. Moreover, in the current scenario of Covid-19, where majority of people cover their faces with masks, potential of recognizing faces gets reduced by a large extent, calling for wide applicability of periocular recognition. In view of these facts, this paper targets towards enhanced representation of near-infrared periocular images, by combined use of hand-crafted and deep features. The hand-crafted features are extracted through partitioning of periocular image followed by obtaining the local statistical properties pertaining to each partition. Whereas, deep features are extracted through the popular convolutional neural network (CNN) ResNet-101 model. The extensive set of experiments performed with a benchmark periocular database validates the promising performance of the proposed method. Additionally, investigation of cross-spectral matching framework and comparison with state-of-the-art, reveal that combination of both types of features employed could prove to be extremely effective.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-021-11846-4