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Heterogeneity aware deep embedding for mobile periocular recognition

  • Rishabh Garg
  • , Yashasvi Baweja
  • , Soumyadeep Ghosh
  • , Richa Singh
  • , Mayank Vatsa
  • , Nalini Ratha
  • Indraprastha Institute of Information Technology Delhi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric patterns in an unconstrained environment including variations in mobile camera sensors, illumination conditions, and capture distance. To address the heterogeneous challenge, this research presents a novel heterogeneity aware loss function within a deep learning framework. The effectiveness of the proposed loss function is evaluated for periocular biometrics using the CSIP, IMP and VISOB mobile periocular databases. The results show that the proposed algorithm yields state-of-the-art results in a heterogeneous environment and improves generalizability for cross-database experiments.

Original languageEnglish
Title of host publication2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538671795
DOIs
StatePublished - Jul 2 2018
Event9th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2018 - Redondo Beach, United States
Duration: Oct 22 2018Oct 25 2018

Publication series

Name2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018

Conference

Conference9th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
Country/TerritoryUnited States
CityRedondo Beach
Period10/22/1810/25/18

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