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Proceedings of 3rd International Conference on Computer Vision and Image Processing pp 131–143Cite as

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

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Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Notes

  1. 1.

    LIBSVM—Provided similar and equivalent performance to SVM from MATLAB from our experimental trials.

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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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Authors and Affiliations

  1. University of South-Eastern Norway (USN), Kongsberg, Norway

    Kiran B. Raja

  2. Norwegian University of Science and Technology (NTNU), Gjøvik, Norway

    Kiran B. Raja, R. Raghavendra & Christoph Busch

Authors
  1. Kiran B. Raja
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  2. R. Raghavendra
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  3. Christoph Busch
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Corresponding author

Correspondence to Kiran B. Raja .

Editor information

Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. Department of Advanced Information Technology and Computer Sciences, Tokyo Institute of Agriculture and Technology, Koganei, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

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Raja, K.B., Raghavendra, R., Busch, C. (2020). Fused Spectral Features in Kernel Weighted Collaborative Representation for Gender Classification Using Ocular Images. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_12

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  • DOI: https://doi.org/10.1007/978-981-32-9088-4_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9087-7

  • Online ISBN: 978-981-32-9088-4

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