nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Unconstrained Iris Segmentation Using Convolutional Neural Networks

verfasst von : Sohaib Ahmad, Benjamin Fuller

Erschienen in: Computer Vision – ACCV 2018 Workshops

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The extraction of consistent and identifiable features from an image of the human iris is known as iris recognition. Identifying which pixels belong to the iris, known as segmentation, is the first stage of iris recognition. Errors in segmentation propagate to later stages. Current segmentation approaches are tuned to specific environments.

We propose using a convolution neural network for iris segmentation. Our algorithm is accurate when trained on a single environment and tested on multiple environments. Our network builds on the Mask R-CNN framework (He et al. ICCV 2017). Our approach segments faster than previous approaches including the Mask R-CNN network.

Our network is accurate when trained on a single environment and tested with a different sensors (either visible light or near-infrared). Its accuracy degrades when trained with a visible light sensor and tested with a near-infrared sensor (and vice versa). A small amount of retraining of the visible light model (using a few samples from a near-infrared dataset) yields a tuned network accurate in both settings.

For training and testing, this work uses the Casia v4 Interval, Notre Dame 0405, Ubiris v2, and IITD datasets.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel PCA-RECT: An Energy-Efficient Object Detection Approach for Event Cameras

Nächstes Kapitel Simultaneous Multi-view Relative Pose Estimation and 3D Reconstruction from Planar Regions

Nur mit Berechtigung zugänglich

Abdullah, M.A., Dlay, S.S., Woo, W.L., Chambers, J.A.: Robust iris segmentation method based on a new active contour force with a noncircular normalization. IEEE Trans. Syst. Man Cybern.: Syst. 47(12), 3128–3141 (2017)CrossRef

Alonso-Fernandez, F., Bigun, J.: Iris boundaries segmentation using the generalized structure tensor. A study on the effects of image degradation. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 426–431. IEEE (2012)

Alonso-Fernandez, F., Bigun, J.: Quality factors affecting iris segmentation and matching. In: 2013 International Conference on Biometrics (ICB), pp. 1–6. IEEE (2013)

Arsalan, M., et al.: Deep learning-based iris segmentation for iris recognition in visible light environment. Symmetry 9(11), 263 (2017)CrossRef

Arsalan, M., Naqvi, R.A., Kim, D.S., Nguyen, P.H., Owais, M., Park, K.R.: IrisDenseNet: robust iris segmentation using densely connected fully convolutional networks in the images by visible light and near-infrared light camera sensors. Sensors 18(5), 1501 (2018)CrossRef

Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRef

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)CrossRef

Bowyer, K.W., Flynn, P.J.: The ND-IRIS-0405 iris image dataset. CoRR abs/1606.04853 (2009)

Daugman, J.: How iris recognition works. In: The Essential Guide to Image Processing, pp. 715–739. Elsevier (2009)

10.

Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)CrossRef

11.

Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MathSciNetCrossRef

12.

Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

13.

Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

14.

He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. IEEE (2017)

15.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

16.

Hofbauer, H., Alonso-Fernandez, F., Wild, P., Bigun, J., Uhl, A.: A ground truth for iris segmentation. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 527–532. IEEE (2014)

17.

Hough, P.V.: Method and means for recognizing complex patterns. US Patent 3,069,654, 18 December 1962

18.

Illingworth, J., Kittler, J.: A survey of the Hough transform. Comput. Vis. Graph. Image Process. 44(1), 87–116 (1988)CrossRef

19.

Jalilian, E., Uhl, A.: Iris segmentation using fully convolutional encoder–decoder networks. In: Bhanu, B., Kumar, A. (eds.) Deep Learning for Biometrics. ACVPR, pp. 133–155. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61657-5_6CrossRef

20.

Jalilian, E., Uhl, A., Kwitt, R.: Domain adaptation for CNN based irissegmentation. In: BIOSIG (2017)

21.

Jeong, D.S., et al.: A new iris segmentation method for non-ideal iris images. Image Vis. Comput. 28(2), 254–260 (2010)CrossRef

22.

Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRef

23.

Kumar, A., Passi, A.: Comparison and combination of iris matchers for reliable personal authentication. Pattern Recogn. 43(3), 1016–1026 (2010)CrossRef

24.

Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, vol. 1, p. 4 (2017)

25.

Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48CrossRef

26.

Liu, N., Li, H., Zhang, M., Liu, J., Sun, Z., Tan, T.: Accurate iris segmentation in non-cooperative environments using fully convolutional networks. In: 2016 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2016)

27.

Proenca, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.: The UBIRIS.v2: a database of visible wavelength images captured on-the-move and at-a-distance. IEEE Trans. PAMI 32(8), 1529–1535 (2010). https://doi.org/10.1109/TPAMI.2009.66CrossRef

28.

Proenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2010)CrossRef

29.

Proença, H., Alexandre, L.A.: The NICE. I: noisy iris challenge evaluation-part I. In: 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–4. IEEE September 2007

30.

Radman, A., Zainal, N., Suandi, S.A.: Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut. Digit. Signal Process. 64, 60–70 (2017)MathSciNetCrossRef

31.

Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

32.

Tan, C.W., Kumar, A.: Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Process. 21(9), 4068–4079 (2012)MathSciNetCrossRef

33.

Tan, C.W., Kumar, A.: Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Process. 22(10), 3751–3765 (2013)MathSciNetCrossRef

34.

Tan, T., Sun, Z.: Casia iris v4 interval. http://biometrics.idealtest.org/

35.

Vezhnevets, V., Konouchine, V.: GrowCut: interactive multi-label ND image segmentation by cellular automata. In: Proceedings of Graphicon, vol. 1, no. 4, pp. 150–156. June 2005

36.

waleedka: Mask R-CNN (2017). https://github.com/matterport/Mask_RCNN

37.

Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)CrossRef

38.

Zhao, Z., Kumar, A.: An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3828–3836. IEEE (2015)

Titel: Unconstrained Iris Segmentation Using Convolutional Neural Networks
verfasst von: Sohaib Ahmad
Benjamin Fuller
Verlag: Springer International Publishing
Buch: Computer Vision – ACCV 2018 Workshops
Print ISBN: 978-3-030-21073-1

Electronic ISBN: 978-3-030-21074-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-21074-8_36

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner