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Local binary hexagonal extrema pattern (LBHXEP): a new feature descriptor for fake iris detection

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Abstract

Security agencies frequently use biometric traits for automatic recognition of a person. The human iris is the most hopeful biometric authentication that can accurately identify a person from their exclusive features. However, in recent years, different types of spoofing attacks are used to violate the security of a biometric system. Biometrics liveness detection system used to recognize persons in a fast and trustworthy way through the use of unique biological distinctiveness. Presentation of a manufactured article of a human iris in the form of photo attack and contact lens attack could hamper the projected policy of a biometric system. The quality of real and fake iris images shows different textural characteristics. In this paper, we have proposed a novel and proficient feature descriptor, i.e., local binary hexagonal extrema pattern for fake iris detection. The proposed descriptor exploits the relationship between the center pixel and its Hexa neighbor. Hexagonal shape using “six-neighbor approach” is preferable to the rectangular structure due to its higher symmetry, consistent connectivity, and efficient use of space. The proposed consideration also solves the “curse of dimensionality” problem in liveness detection. The proposed descriptor is evaluated on ATVS-FIr DB and IIIT-D CLI databases for iris liveness detection and show promising performance for liveness detection in terms green, brown, etc. of accuracy and average error rate.

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References

  1. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)

    Article  Google Scholar 

  2. ISO/IEC CD 30107-1. Information Technology—biometrics—presentation attack detection

  3. https://en.wikipedia.org/wiki/Iris_(anatomy)

  4. Nguyen, K., Fookes, C., Jillela, R., Sridharan, S., Ross, A.: Long range iris recognition: a survey. Pattern Recognit. 72, 123–143 (2017)

    Article  Google Scholar 

  5. Sharma, R.P., Dey, S.: Fingerprint liveness detection using local quality features. Vis. Comput. 35(10), 1393–1410 (2019)

    Article  Google Scholar 

  6. He, Z., Sun, Z., Tan, T., Wei, Z.: Efficient iris spoof detection via boosted local binary patterns. In Proceedings of international conference on biometrics, pp. 1080–1090 (2009)

  7. Li, C., Zhou, W., Yuan, S.: Iris recognition based on a novel variation of local binary pattern. Vis. Comput. 31(10), 1419–1429 (2015)

    Article  Google Scholar 

  8. Zhang, H., Sun, Z., Tan, T.: Contact lens detection based on weighted LBP. In: Proceedings of 20th IEEE International Conference on Pattern Recognition, pp. 4279–4282 (2010)

  9. Galbally, J., Marcel, S., Fierrez, J.: Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans. Image Process. 23(2), 710–724 (2014)

    Article  MathSciNet  Google Scholar 

  10. Raghavendra, R., Busch, C.: Robust scheme for iris presentation attack detection using multiscale binarized statistical image features. IEEE Trans. Inf. Forensics Secur. 10(4), 703–715 (2015)

    Article  Google Scholar 

  11. Hu, Y., Sirlantzis, K., Howells, G.: Iris liveness detection using regional features. Pattern Recognit. Lett. 82, 242–250 (2016)

    Article  Google Scholar 

  12. Dubey, S.R., Singh, S.K., Singh, R.K.: Local diagonal extrema pattern: a new and efficient feature descriptor for CT image retrieval. IEEE Signal Process. Lett. 22(9), 1215–1219 (2015)

    Article  Google Scholar 

  13. He, X., Lu, Y., Shi, P.: A fake iris detection method based on FFT and quality assessment. In: Proceedings of IEEE Chinese Conference on Pattern Recognition, pp. 1–4 (2008)

  14. Galbally, J., Gomez-Barrero, M.: A review of iris anti-spoofing. In: Proceedings of 4th IEEE International Conference on Biometrics and Forensics (IWBF), pp. 1–6 (2016)

  15. Daugman, J.: How iris recognition works. In: The essential guide to image processing, pp. 715–739 (2009)

  16. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 7, 971–987 (2002)

    Article  Google Scholar 

  17. Ojansivu, V., Rahtu, E., Heikkila, J.: Rotation invariant local phase quantization for blur insensitive texture analysis. In: Proceedings of 19th IEEE International Conference on Pattern Recognition, pp. 1–4 (2008)

  18. Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2009)

    Article  Google Scholar 

  19. Nosaka, R., Ohkawa, Y., Fukui, K.: Feature extraction based on co-occurrence of adjacent local binary patterns. In: Pacific-Rim Symposium on Image and Video Technology. Springer, Berlin, Heidelberg, pp. 82–91 (2011)

  20. Kannala, J., Rahtu, E.: Bsif: Binarized statistical image features. In: Proceedings of the IEEE 21st International Conference on Pattern Recognition, pp. 1363–1366 (2012)

  21. He, X., An, S., Shi, P.: Statistical texture analysis-based approach for fake iris detection using support vector machines. In: Proceedings of International Conference on Biometrics. Springer, Berlin, Heidelberg, pp. 540–546 (2007)

  22. He, X., Lu, Y., Shi, P.: A new fake iris detection method. In: International Conference on Biometrics, Springer, Berlin, Heidelberg, pp. 1132–1139 (2009)

  23. Galbally, J., Ortiz-Lopez, J., Fierrez, J., Ortega-Garcia, J.: Iris liveness detection based on quality related features. In: Proceedings of 5th IEEE APR International Conference on Biometrics (ICB), pp. 271–276 (2012)

  24. Chen, R., Lin, X., Ding, T.: Liveness detection for iris recognition using multispectral images. Pattern Recognit. Lett. 33(12), 1513–1519 (2012)

    Article  Google Scholar 

  25. Connell, J., Ratha, N., Gentile, J., Bolle, R.: Fake iris detection using structured light. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8692–8696 (2013)

  26. Kohli, N., Yadav, D., Vatsa, M., Singh, R., Noore, A.: Detecting medley of iris spoofing attacks using DESIST. In: Proceedings of IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–6 (2016)

  27. Bhogal, A.P.S., Söllinger, D., Trung, P., Uhl, A.: Non-reference image quality assessment for biometric presentation attack detection. In: Proceedings of IEEE 5th International Workshop on Biometrics and Forensics, pp. 1–6 (2017)

  28. Fathy, W.S.A., Ali, H.S.: Entropy with local binary patterns for efficient iris liveness detection. Wirel. Pers. Commun. 102(3), 2331–2344 (2018)

    Article  Google Scholar 

  29. Chen, C., Ross, A.: A multi-task convolutional neural network for joint iris detection and presentation attack detection. In: Proceedings of the IEEE Conference on Winter Applications of Computer Vision Workshops (WACVW), pp. 44–51 (2018)

  30. Liu, M., Zhou, Z., Shang, P., Xu, D.: Fuzzified image enhancement for deep learning in iris recognition. IEEE Trans. Fuzzy Syst. 28(1), 92–99 (2020)

    Article  Google Scholar 

  31. Choudhary, M., Tiwari, V., Venkanna, U.: An approach for iris contact lens detection and classification using ensemble of customized DenseNet and SVM. Future Gener. Comput. Syst. 101, 1259–1270 (2019)

    Article  Google Scholar 

  32. Long, M., Zeng, Y.: Detecting iris liveness with batch normalized convolutional neural network. Comput. Mater. Contin. 58(2), 493–504 (2019)

    Article  Google Scholar 

  33. Chatterjee, P., Yalchin, A., Shelton, J., Roy, K., Yuan, X., Edoh, K.D.: Presentation attack detection using wavelet transform and deep residual neural net. In: Proceedings of Springer Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, pp. 86–94 (2019)

  34. Johar, T., Kaushik, P.: Iris segmentation and normalization using Daugman’s rubber sheet model. Int. J. Sci. Tech. Adv. 1(3), 11–14 (2015)

    Google Scholar 

  35. Yan, C., Wang, Z.Z., Gao, Q.B., Du, Y.H.: A novel kernel for sequences classification. In: Proceedings of IEEE International Conference on Natural Language Processing and Knowledge Engineering, pp. 769–773 (2005)

  36. 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)

    Article  Google Scholar 

  37. Pala, F., Bhanu, B.: Iris liveness detection by relative distance comparisons. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 162–169 (2017)

  38. Tola, E., Lepetit, V., Fua, P.: Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2009)

    Article  Google Scholar 

  39. Gragnaniello, D., Poggi, G., Sansone, C., Verdoliva, L.: An investigation of local descriptors for biometric spoofing detection. Pattern Recognit. 48(4), 1050–1058 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, 281406, India

    Rohit Agarwal & Anand Singh Jalal

  2. ICT, ABV-IIITM, Gwalior, Madhya Pradesh (Right), 474015, India

    K. V. Arya

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  1. Rohit Agarwal
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  2. Anand Singh Jalal
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  3. K. V. Arya
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Correspondence to Rohit Agarwal.

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Agarwal, R., Jalal, A.S. & Arya, K.V. Local binary hexagonal extrema pattern (LBHXEP): a new feature descriptor for fake iris detection. Vis Comput 37, 1357–1368 (2021). https://doi.org/10.1007/s00371-020-01870-0

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