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

Vyas, R. (2022) Enhanced near-infrared periocular recognition through collaborative rendering of hand crafted and deep features. Multimedia Tools and Applications. ISSN 1380-7501

[img]
Text (template_revised_2)
template_revised_2.pdf - Accepted Version
Restricted to Repository staff only until 18 January 2023.
Available under License Other.

Download (619kB)

Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Multimedia Tools and Applications
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-021-11846-4
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1705
Subjects:
ID Code:
165615
Deposited By:
Deposited On:
07 Feb 2022 15:10
Refereed?:
Yes
Published?:
Published
Last Modified:
04 May 2022 02:41