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access icon free Efficient multimodal ocular biometric system for person authentication based on iris texture and corneal shape

  • Author(s): Nassima Kihal 1, 2, 3 1, 2, 3 ; Salim Chitroub 3 1 ; Arnaud Polette 2, 4 2, 4 ; Isabelle Brunette 5 5 ; Jean Meunier 2, 6 2, 6
    • View affiliations
    • Affiliations: 1: Laboratory of Automatic of Jijel (LAJ), University of Jijel, Algeria ;
      2: Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada ;
      3: Signal and Image Processing Laboratory Electronics and Computer Science Faculty, USTHB, Algiers, Algeria ;
      4: Aix-Marseille University, CNRS, LSIS UMR 7296, France ;
      5: Maisonneuve-Rosemont Hospital, Department of Ophthalmology, University of Montreal, Montreal, Canada ;
      6: Biomedical Engineering Institute, University of Montreal, Montreal, Canada
  • Source: Volume 6, Issue 6, November 2017, p. 379 – 386
    DOI:  10.1049/iet-bmt.2016.0067 , Print ISSN 2047-4938, Online ISSN 2047-4946
© The Institution of Engineering and Technology
Received 21/04/2016, Accepted 11/01/2017, Revised 17/10/2016, Published 18/01/2017

Ocular biometrics refers to the use of features of the eye for person recognition. For instance, the unique and stable texture of the iris has been recognised as a powerful ocular biometric characteristic. In this study, the authors propose to improve biometric authentication with a multimodal ocular biometric system based on the iris pattern and the three-dimensional shape of the cornea. They show how the cornea can be used as a biometric trait for person recognition and then, they propose an intra-ocular fusion with iris features to improve the overall performance of the system. Feature extraction was done by modelling the shape of the cornea with a Zernike polynomial expansion. Then the best linear combinations of Zernike coefficients were found with linear discriminant analysis and used as biometric identifier. The iris texture was analysed with a typical methodology using Gabor filtering and phase encoding. The fusion was performed at the matching score level using min, max, sum and weighted-sum rule. The experimental results on a new database constructed for this bi-modal study showed impressive performance of the proposed ocular biometric system with equal error rate decreasing to 0% with the weighted-sum rule.

Inspec keywords: minimax techniques; image matching; feature extraction; Gabor filters; image coding; iris recognition; shape recognition; image fusion; Zernike polynomials; image filtering; image texture

Other keywords: Zernike polynomial expansion; Zernike coefficients; Gabor filtering; ocular biometric characteristic; three-dimensional cornea shape modelling; intra-ocular fusion; iris pattern; linear discriminant analysis; matching score level; biometric authentication; iris feature extraction; biometric identifier; person recognition; weighted-sum rule; min-max rule; iris texture; multimodal ocular biometric system; phase encoding

Subjects: Image and video coding; Algebra; Algebra; Optimisation techniques; Optimisation techniques; Filtering methods in signal processing; Image recognition; Computer vision and image processing techniques

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