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Fused Spectral Features in Kernel Weighted Collaborative Representation for Gender Classification Using Ocular Images

  • Kiran B. RajaEmail author
  • R. Raghavendra
  • Christoph Busch
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)

Abstract

Ocular images have been used to supplement and complement the face-based biometrics. Ocular images are further investigated for identifying the gender of a person such that the soft label can be used to boost biometric performance of the system. Although there are number of works in visible spectrum and Near-Infrared spectrum for gender classification using ocular images, there are limited works in spectral imaging which explore ocular images for gender classification. Considering the advantages of spectral imaging, we explore the problem of gender identification using ocular images obtained using spectral imaging. To this end, we have employed a recent database of 104 unique ocular instances across 2 different sessions and 5 different attempts in each session with a spectral imaging camera capable of capturing 8 different images corresponding to different bands. Further, we present a new framework of using fused feature descriptors in kernalized space to fully leverage the number of spectral images for robust gender classification. With the set of experiments, we obtain an average classification accuracy of \(81\%\) with the proposed approach of using fused GIST features along with the weighted kernel representation of features in collaborative space.

Notes

Acknowledgements

This work was carried out under the funding of the Research Council of Norway under Grant No. IKTPLUSS 248030/O70. Ethical and privacy perspectives for the study were taken into consideration according to the national guidelines for this work.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Kiran B. Raja
    • 1
    • 2
    Email author
  • R. Raghavendra
    • 2
  • Christoph Busch
    • 2
  1. 1.University of South-Eastern Norway (USN)KongsbergNorway
  2. 2.Norwegian University of Science and Technology (NTNU)GjøvikNorway

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