Robust Detection of Iris Region Using an Adapted SSD Framework

  • Saksham JainEmail author
  • Indu Sreedevi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1019)


Accurate detection of the iris is a crucial step in several biometric tasks, such as iris recognition and spoofing detection, among others. In this paper, we consider the detection task to be the delineation of the smallest square bounding box that surrounds the iris region. To overcome the various challenges of the iris detection task, we present an efficient iris detection method that leverages the SSD (Single Shot multibox Detector) model. The architecture of SSD is modified to give a lighter and simpler framework capable of performing fast and accurate detection on the relatively smaller sized iris biometric datasets. Our method is evaluated on 4 datasets taken from different biometric applications and from the literature. It is also compared with baseline methods, such as Daugman’s algorithm, HOG+SVM and YOLO. Experimental results show that our modified SSD outperforms these other techniques in terms of speed and accuracy. Moreover, we introduce our own near-infrared image dataset for iris biometric applications, containing a robust range of samples in terms of age, gender, contact lens presence, and lighting conditions. The models are tested on this dataset, and shown to generalise well. We also release this dataset for use by the scientific community.


Biometrics Iris detection SSD 


  1. 1.
    Arsalan, M., et al.: Deep learning-based iris segmentation for iris recognition in visible light environment. Symmetry 9(11), 263 (2017). Scholar
  2. 2.
    Bazrafkan, S., Thavalengal, S., Corcoran, P.: An end to end deep neural network for iris segmentation in unconstrained scenarios. Neural Netw. 106, 79–95 (2018)CrossRefGoogle Scholar
  3. 3.
    CBSR: Casia-irisv3 image database.
  4. 4.
    Chen, C., Ross, A.: A multi-task convolutional neural network for joint iris detection and presentation attack detection. In: 2018 IEEE Winter Applications of Computer Vision Workshops (WACVW), pp. 44–51, March 2018.
  5. 5.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893, June 2005.
  6. 6.
    Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004). Scholar
  7. 7.
    Doyle, J.S., Bowyer, K.W.: Robust detection of textured contact lenses in iris recognition using BSIF. IEEE Access 3, 1672–1683 (2015). Scholar
  8. 8.
    Doyle, J.S., Bowyer, K.W., Flynn, P.J.: Variation in accuracy of textured contact lens detection based on sensor and lens pattern. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–7, Sept 2013.
  9. 9.
    Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp. 2155–2162. CVPR ’14, IEEE Computer Society, Washington, DC, USA (2014).
  10. 10.
    Fawzi, A., Moosavi-Dezfooli, S., Frossard, P.: The robustness of deep networks: a geometrical perspective. IEEE Signal Process. Mag. 34(6), 50–62 (2017). Scholar
  11. 11.
    Han, M., Sun, W., Li, M.: Iris recognition based on a novel normalization method and contourlet transform. In: 2009 2nd International Congress on Image and Signal Processing, pp. 1–3, October 2009.
  12. 12.
    Hinton, G., Vinyals, O., Dean, J.: Distilling the Knowledge in a Neural Network. ArXiv e-prints, March 2015Google Scholar
  13. 13.
    Kohli, N., Yadav, D., Vatsa, M., Singh, R.: Revisiting iris recognition with color cosmetic contact lenses. In: 2013 International Conference on Biometrics (ICB), pp. 1–7, June 2013.
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1., pp. 1097–1105. Curran Associates Inc., USA (2012).
  15. 15.
    Liu, W., et al.: SSD: Single Shot MultiBox Detector. ArXiv e-prints, December 2015Google Scholar
  16. 16.
    Menotti, D., et al.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015). Scholar
  17. 17.
    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). Scholar
  18. 18.
    Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724, June 2014.
  19. 19.
    Radman, A., Jumari, K., Zainal, N.: Fast and reliable iris segmentation algorithm. IET Image Process. 7(1), 42–49 (2013). Scholar
  20. 20.
    Ramkumar, R.P., Arumugam, S.: A novel iris recognition algorithm. In: 2012 Third International Conference on Computing, Communication and Networking Technologies, ICCCNT 2012, pp. 1–6, July 2012.
  21. 21.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified. Real-Time Object Detection, ArXiv e-prints, June 2015Google Scholar
  22. 22.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). Scholar
  23. 23.
    Rodríguez, J.L.G., Rubio, Y.D.: A new method for iris pupil contour delimitation and its application in iris texture parameter estimation. In: Sanfeliu, A., Cortés, M.L. (eds.) Progress in Pattern Recognition, Image Analysis and Applications, pp. 631–641. Springer, Heidelberg (2005). Scholar
  24. 24.
    Severo, E., et al.: A Benchmark for Iris Location and a Deep Learning Detector Evaluation. ArXiv e-prints, March 2018Google Scholar
  25. 25.
    Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. ArXiv e-prints, September 2014Google Scholar
  26. 26.
    Su, L., Wu, J., Li, Q., Liu, Z.: Iris location based on regional property and iterative searching. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1064–1068, August 2017.
  27. 27.
    Tang, Y., Eliasmith, C.: Deep networks for robust visual recognition. In: ICML (2010)Google Scholar
  28. 28.
    Tisse, C.L., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: Proceedings of Vision Interface, pp. 294–299 (2002)Google Scholar
  29. 29.
    Tsai, C., Lin, H., Taur, J., Tao, C.: Iris recognition using possibilistic fuzzy matching on local features. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(1), 150–162 (2012). Scholar
  30. 30.
    Yadav, D., Kohli, N., Doyle, J.S., Singh, R., Vatsa, M., Bowyer, K.W.: Unraveling the effect of textured contact lenses on iris recognition. IEEE Trans. Inf. Forensics Secur. 9(5), 851–862 (2014). Scholar
  31. 31.
    Yi, J., Wu, P., Hoeppner, D.J., Metaxas, D.: Fast neural cell detection using light-weight SSD neural network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 860–864, July 2017.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Netaji Subhas Institute of TechnologyDelhiIndia
  2. 2.Delhi Technological UniversityDelhiIndia

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