Improving periocular recognition by explicit attention to critical regions in deep neural network

Zijing Zhao, Ajay Kumar

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)

Abstract

Periocular recognition has been emerging as an effective biometric identification approach, especially under less constrained environments where face and/or iris recognition is not applicable. This paper proposes a new deep learning-based architecture for robust and more accurate periocular recognition which incorporates attention model to emphasize important regions in the periocular images. The new architecture adopts multi-glance mechanism, in which part of the intermediate components are configured to incorporate emphasis on important semantical regions, i.e., eyebrow and eye, within a periocular image. By focusing on these regions, the deep convolutional neural network is able to learn additional discriminative features, which in turn improves the recognition capability of the whole model. The superior performance of our method strongly suggests that eyebrow and eye regions are important for periocular recognition, and deserve special attention during the deep feature learning process. This paper also presents a customized verification-oriented loss function, which is shown to provide higher discriminating power than conventional contrastive/triplet loss functions. Extensive experiments on six publicly available databases are performed to evaluate the proposed approach. The reproducible experimental results indicate that our approach significantly outperforms several state-of-the-art methods for the periocular recognition.

Original languageEnglish
Pages (from-to)2937-2952
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Volume13
Issue number12
DOIs
Publication statusPublished - Dec 2018

Keywords

  • attention model
  • deep learning
  • Periocular recognition
  • region of interest

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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