Accurate Periocular Recognition under Less Constrained Environment Using Semantics-Assisted Convolutional Neural Network

Zijing Zhao, Ajay Kumar Pathak

Research output: Journal article publicationJournal articleAcademic researchpeer-review

75 Citations (Scopus)


Accurate biometric identification under real environments is one of the most critical and challenging tasks to meet growing demand for higher security. This paper proposes a new framework to efficiently and accurately match periocular images that are automatically acquired under less-constrained environments. Our framework, referred to as semantics-assisted convolutional neural networks (SCNNs) in this paper, incorporates explicit semantic information to automatically recover comprehensive periocular features. This strategy enables superior matching accuracy with the usage of relatively smaller number of training samples, which is often an issue with several biometrics. Our reproducible experimental results on four different publicly available databases suggest that the SCNN-based periocular recognition approach can achieve outperforming results, both in achievable accuracy and matching time, for less-constrained periocular matching. Additional experimental results presented in this paper also indicate that the effectiveness of proposed SCNN architecture is not only limited to periocular recognition but it can also be useful for generalized image classification. Without increasing the volume of training data, the SCNN is able to automatically extract more discriminative features from the input data than a single CNN, therefore can consistently improve the recognition performance. The experimental results presented in this paper validate such an approach to enable faster and more accurate periocular recognition under less constrained environments.
Original languageEnglish
Article number7775081
Pages (from-to)1017-1030
Number of pages14
JournalIEEE Transactions on Information Forensics and Security
Issue number5
Publication statusPublished - 1 May 2017


  • convolution neural network
  • deep learning
  • Periocular recognition
  • training data augmentation

ASJC Scopus subject areas

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

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