CNN Based Periocular Recognition Using Multispectral Images

  • Vineetha Mary IpeEmail author
  • Tony Thomas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1209)


Over the recent years, the periocular region has emerged as a potential unconstrained biometric trait for person authentication. For a biometric identification scenario to operate reliably round the clock, it should be capable of subject recognition in multiple spectra. However, there is limited research associated with the non-ideal multispectral imaging of the periocular trait. This is critical for real life applications such as surveillance and watch list identification. The existing techniques for multispectral periocular recognition rely on fusion at the feature level. However, these handcrafted features are not primarily data driven and there even exists possibilities for more novel features that could better describe the same. One possible solution to address such issues is to resort to the data driven deep learning strategies. Accordingly, we propose to apply the attributes extracted from pretrained CNN for subject authentication. To the best of our knowledge, this is the first study of multispectral periocular recognition employing deep learning. For our work, the IIITD Multispectral Periocular (IMP) database is used. The best classification accuracy reported for this dataset is 91.8%. This value is not precise enough for biometric identification tasks. The off-the-shelf CNN features employed in our work gives an improved accuracy of 97.14% for the multispectral periocular images.


Multispectral periocular recognition Deep learning Biometrics Convolutional neural network (CNN) 



This work is done as a part of the project CEPIA (Centre of Excellence in Pattern and Image Analysis) 2019–20, which is funded by the Kerala state planning board.


  1. 1.
    Algashaam, F.M., Nguyen, K., Alkanhal, M., Chandran, V., Boles, W., Banks, J.: Multispectral periocular classification with multimodal compact multi-linear pooling. IEEE Access 5, 14572–14578 (2017)CrossRefGoogle Scholar
  2. 2.
    Alonso-Fernandez, F., Bigun, J.: A survey on periocular biometrics research. Pattern Recogn. Lett. 82, 92–105 (2016)CrossRefGoogle Scholar
  3. 3.
    Bakshi, S., Sa, P.K., Majhi, B.: A novel phase-intensive local pattern for periocular recognition under visible spectrum. Biocybern. Biomed. Eng. 35(1), 30–44 (2015)CrossRefGoogle Scholar
  4. 4.
    Bharadwaj, S., Bhatt, H.S., Vatsa, M., Singh, R.: Periocular biometrics: when iris recognition fails. In: 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) (2010)Google Scholar
  5. 5.
    Hernandez-Diaz, K., Alonso-Fernandez, F., Bigun, J.: Periocular recognition using CNN features off-the-shelf. In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG) (2018)Google Scholar
  6. 6.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). Scholar
  8. 8.
    Luz, E., Moreira, G., Junior, L.A.Z., Menotti, D.: Deep periocular representation aiming video surveillance. Pattern Recogn. Lett. 114, 2–12 (2018)CrossRefGoogle Scholar
  9. 9.
    Namatēvs, I.: Deep convolutional neural networks: structure, feature extraction and training. Inf. Technol. Manag. Sci. 20(1), 40–47 (2017)Google Scholar
  10. 10.
    Nguyen, K., Fookes, C., Ross, A., Sridharan, S.: Iris recognition with off-the-shelf CNN features: a deep learning perspective. IEEE Access 6, 18848–18855 (2018)CrossRefGoogle Scholar
  11. 11.
    Nguyen, K., Fookes, C., Sridharan, S., Tistarelli, M., Nixon, M.: Super-resolution for biometrics: a comprehensive survey. Pattern Recognit. 78, 23–42 (2018)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)zbMATHCrossRefGoogle Scholar
  13. 13.
    Park, U., Jillela, R., Ross, A., Jain, A.: Periocular biometrics in the visible spectrum. IEEE Trans. Inf. Forensics Secur. 6(1), 96–106 (2011)CrossRefGoogle Scholar
  14. 14.
    Park, U., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum: a feasibility study. In: 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems (2009)Google Scholar
  15. 15.
    Proenca, H., Neves, J.C.: Deep-PRWIS: periocular recognition without the iris and sclera using deep learning frameworks. IEEE Trans. Inf. Forensics Secur. 13(4), 888–896 (2018)CrossRefGoogle Scholar
  16. 16.
    Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)Google Scholar
  17. 17.
    Sharma, A., Verma, S., Vatsa, M., Singh, R.: On cross spectral periocular recognition. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5007–5011. IEEE (2014)Google Scholar
  18. 18.
    Tapia, J., Viedma, I.: Gender classification from multispectral periocular images. In: 2017 IEEE International Joint Conference on Biometrics (IJCB) (2017)Google Scholar
  19. 19.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR (2001) Google Scholar
  20. 20.
    Zhang, D., Guo, Z., Gong, Y.: Multispectral biometrics systems. In: Zhang, D., Guo, Z., Gong, Y. (eds.) Multispectral Biometrics, pp. 23–35. Springer, Cham (2015). Scholar
  21. 21.
    Zhang, S., Wang, X.: Human detection and object tracking based on histograms of oriented gradients. In: 2013 Ninth International Conference on Natural Computation (ICNC) (2013)Google Scholar
  22. 22.
    Zhao, Z., Kumar, A.: Accurate periocular recognition under less constrained environment using semantics-assisted convolutional neural network. IEEE Trans. Inf. Forensics Secur. 12(5), 1017–1030 (2017)CrossRefGoogle Scholar
  23. 23.
    Haghighat, M., Abdel-Mottaleb, M., Alhalabi, W.: Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Syst. Appl. 47, 23–34 (2016)CrossRefGoogle Scholar
  24. 24.
    Haghighat, M., Abdel-Mottaleb, M., Alhalabi, W.: Discriminant correlation analysis for feature level fusion with application to multimodal biometrics. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Indian Institute of Information Technology and ManagementKeralaIndia

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