Skip to main content
Springer Nature Link
Log in

Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Obfuscating an iris recognition system through forged iris samples has been a major security threat in iris-based authentication. Therefore, a detection mechanism is essential that may explicitly discriminate between the live iris and forged (attack) patterns. The majority of existing methods analyze the eye image as a whole to find discriminatory features for fake and real iris. However, many attacks do not alter the entire eye image, instead merely the iris region is affected. It infers that the iris embodies the region of interest (RoI) for an exhaustive search towards identifying forged iris patterns. This paper introduces a novel framework that locates RoI using the YOLO approach and performs selective image enhancement to enrich the core textural details. The YOLO approach tightly bounds the iris region without any pattern loss, where the textural analysis through local and global descriptors is expected to be efficacious. Afterward, various handcrafted and CNN based methods are employed to extract the discriminative textural features from the RoI. Later, the best-k features are identified through the Friedman test as the optimal feature set and combined using score-level fusion. Further, the proposed approach is assessed on six different iris databases using predefined intra-dataset, cross-dataset, and combined-dataset validation protocols. The experimental outcomes exhibit that the proposed method results in significant error reduction with the state of the arts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Choudhary M, Tiwari V, Venkanna U (2019) Enhancing human iris recognition performance in unconstrained environment using ensemble of convolutional and residual deep neural network models. Soft Comput. https://doi.org/10.1007/s00500-019-04610-2

    Article  Google Scholar 

  2. Czajkaand A, Bowyer KW (2018) Presentation attack detection for iris recognition: an assessment of the state of the art. ACM Comput Surv 51(4):86-1–86-35

    Google Scholar 

  3. Hu Y, Sirlantzis K, Howells G (2016) Iris liveness detection using regional features. Pattern Recogn Lett 82(02):242–250

    Article  Google Scholar 

  4. Rigas I, Komogortsev OV (2015) Eye movement-driven defense against iris print-attacks. Pattern Recogn Lett 68(2):316–326

    Article  Google Scholar 

  5. Lee EC, Park KR, Kim J (2006) Fake iris detection by using purkinje image. In: Proceedings of the international conference on advances on biometrics (ICB’06), of lecture notes in computer science. Springer, Hong Kong, vol 3832, pp 397–403

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

    Article  Google Scholar 

  7. He Z, Sun Z, Tan T, Wei Z (2009) Efficient iris spoof detection via boosted local binary patterns. In: Proceeding of ICB, pp 1080–1090

  8. Doyle JS, Bowyer KW (2015) Robust detection of textured contact lenses in iris recognition using BSIF. IEEE Access 3:1672–1683

    Article  Google Scholar 

  9. Kokkinos I, Bronstein MM, Yuille A (2012) Dense scale invariant descriptors for images and surface. Research report rr-7914, INRIA

  10. Yambay D et al (2017) LivDet iris 2017 Iris liveness detection competition 2017. In: Proceeding of 2017 IEEE international joint conference on biometrics (IJCB), Denver, CO, pp 733–741

  11. Chen C, Ross A (2018) A multi-task convolutional neural network for joint iris detection and presentation attack detection. In: Proceeding of IEEE winter applications of computer vision workshops (WACVW), Lake Tahoe, NV, pp 44–51

  12. Kuehlkamp A, Pinto A, Rocha A, Bowyer KW, Czajka A (2018) Ensemble of multi-view learning classifiers for cross-domain iris presentation attack detection. IEEE Trans Inf Forensics Secur 14(6):1419–1431

    Article  Google Scholar 

  13. Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceeding of CVPR arXiv:1612.08242v1

  14. He F, Han Y, Wang H, Ji J, Liu Y, Ma Z (2017) Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network. J Electron Imaging 26(2):023005

    Article  Google Scholar 

  15. Pei Y, Huang Y, Zou Q, Zang H, Zhang X, Wang S (2018) Effects of image degradations to cnn-based image classification. arXiv preprint arXiv:1810.05552

  16. Zhao Z, Kumar A (2015) An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In: Proceedings of the IEEE international conference on computer vision (ICCV). IEEE, pp 3828–3836

  17. Zhao Z, Kumar A (2019) A deep learning based unified framework to detect, segment and recognize irises using spatially corresponding features. Pattern Recogn 93:546–557

    Article  Google Scholar 

  18. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

  19. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

  20. Akilan T, Wu QJ, Zhang H (2018) Effect of fusing features from multiple DCNN architectures in image classification. IET Image Proc 12(7):1102–1110. https://doi.org/10.1049/iet-ipr.2017.0232

    Article  Google Scholar 

  21. Poster D, Nasrabadi N, Riggan B (2018) Deep sparse feature selection and fusion for textured contact lens detection. In: Proceeding of international conference of the biometrics special interest group (BIOSIG), Darmstadt, pp 1–5

  22. Yadav D, Kohli N, Agarwal A, Vatsa M, Singh R, Noore A (2018) Fusion of handcrafted and deep learning features for large-scale multiple iris presentation attack detection. In: Proceeding of IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), Salt Lake City, UT, pp 685–6857

  23. Choudhary M, Tiwari, V, Venkanna, U (2020) Iris anti-spoofing through score-level fusion of handcrafted and data-driven features. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106206

    Article  Google Scholar 

  24. Czajka A (2015) Pupil dynamics for iris liveness detection. IEEE Trans Inf Forensics Secur 10(4):726–735

    Article  Google Scholar 

  25. Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) An investigation of local descriptors for biometric spoofing detection. IEEE Trans Inf Forensics Secur 10(4):849–863

    Article  Google Scholar 

  26. Li J, Allinson NM (2008) A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12):1771–1787

    Article  Google Scholar 

  27. Daugman J (2003) Demodulation by complex-valued wavelets for stochastic pattern recognition. Int J Wavel Multi-resolut Inform Process 1(1):1–17

    Article  Google Scholar 

  28. Nosaka R, Ohkawa Y, Fukui K (2011) Feature extraction based on co-occurrence of adjacent local binary patterns. In: Proceeding of Pacific-Rim symposium on image and video technology. Springer, pp 82–91

  29. Tola E, Lepetit V, Fua P (2010) Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32(5):815–830

    Article  Google Scholar 

  30. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceeding of IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 886–893

  31. Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2016) Using iris and sclera for detection and classification of contact lenses. Pattern Recogn Lett 82(2):251–257

    Article  Google Scholar 

  32. Hsieh SH, Li Y, Wang W, Tien C (2018) A novel anti-spoofing solution for iris recognition toward cosmetic contact lens attack using spectral ICA analysis. Sensors (Basel) 18(3):1–15

    Article  Google Scholar 

  33. Sharifi O, Eskandari M (2018) Cosmetic detection framework for face and iris biometrics. Symmetry 10(4):122-1–122-9

    Article  Google Scholar 

  34. Menotti D et al (2015) Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans Inf Forensics Secur 10(4):864–879

    Article  Google Scholar 

  35. Silva P, Luz E, Baeta R, Pedrini H, Falcao AX, Menotti D (2015) An approach to iris contact lens detection based on deep image representations. In: Proceeding of 28th SIBGRAPI conference on graphics, patterns and images, Salvador, pp 157–164

  36. He L, Li H, Liu F, Liu N, Sun Z, He Z (2016) Multi-patch convolution neural network for iris liveness detection. In: Proceeding of IEEE 8th international conference on biometrics theory, applications and systems (BTAS), Niagara Falls, NY, pp 1–7

  37. Kohli N, Yadav D, Vatsa M, Singh R, Noore A (2016) Detecting medley of iris spoofing attacks using DESIST. In: Proceeding of IEEE 8th international conference on biometrics theory, applications and systems (BTAS), Niagara Falls, NY, pp 1–6

  38. Nguyen DT, Pham TD, Lee YW, Park KR (2018) Deep learning-based enhanced presentation attack detection for iris recognition by combining features from local and global regions based on NIR camera sensor. Sensors (Basel) 18(8):2601-1–2601-32

    Google Scholar 

  39. Demsar Janez (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  40. Chollet F et al (2015) Keras. https://github.com/fchollet/keras

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

    Article  Google Scholar 

  42. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Vis Pattern Recognit. arXiv:1409.1556v6

  43. Czajka A, Moreira D, Bowyer K, Flynn P (2019) Domain-specific human-inspired binarized statistical image features for iris recognition. In: 2019 IEEE winter conference on applications of computer vision (WACV). IEEEpp. 959–967

  44. Tan CW, Kumar A (2014) Accurate iris recognition at a distance using stabilized iris encoding and Zernike moments phase features. IEEE Trans Image Process 23(9):3962–3974

    Article  MathSciNet  Google Scholar 

  45. Yadav D, Kohli N, Doyle JS, Singh R, Vatsa M, Bowyer KW (2014) Unraveling the effect of textured contact lenses on iris recognition. IEEE Trans Inf Forensics Secur 9(5):851–862

    Article  Google Scholar 

  46. Doyle J, Bowyer KW (2014) Notre Dame image dataset for contact lens detection in iris recognition. In: Rathgeb C, Busch C (eds) Iris and periocular biometric recognition, Chapter: 12. Institution of Engineering and Technology (IET), London, pp 265–290

    Google Scholar 

  47. Available: https://www.iso.org/committee/313770. Accessed on June 2019

  48. Yadav D, Kohli N, Vatsa M, Singh R, Noore A (2019) Detecting textured contact lens in uncontrolled environment using DensePAD. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops

Download references

Author information

Authors and Affiliations

  1. DSPM IIIT Naya Raipur, Naya Raipur, CG, 493661, India

    Meenakshi Choudhary, Vivek Tiwari & Venkanna Uduthalapally

Authors
  1. Meenakshi Choudhary
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Vivek Tiwari
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Venkanna Uduthalapally
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Vivek Tiwari.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Choudhary, M., Tiwari, V. & Uduthalapally, V. Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI. Neural Comput & Applic 33, 5609–5629 (2021). https://doi.org/10.1007/s00521-020-05342-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05342-3

Keywords