Abstract
The paper presents an efficient lightweight U-net convolutional neural network (CNN) architecture that can be used for iris segmentation in eye images. The novelty of the proposed method consists of model downscaling for efficiency, while maintaining high iris segmentation accuracy. The network is validated on different resolution and quality images from five standard open source benchmarks: BioSec, CasiaI4, CasiaT4, IITD, and UBIRIS. The efficient U-net architecture consists of 36 layers and uses 148 k parameters, value order of magnitude lower than other existing networks used for similar applications. This also leads to a much lower training time and eye image segmentation time (< 1 ms per image on Xeon CPU). The iris segmentation results obtained were state-of-the-art in terms of standard nice1, F1 and mIoU accuracy measures on all the analyzed datasets. Whilst some differences can be observed for these measurements between datasets, the lowest values for F1 and mIoU parameters obtained were 96.14% and 92.56%, respectively, on UBIRIS dataset, and for nice1 parameter 0.38 on CasiaT4. The best results were obtained on CasiaI4 dataset with F1 = 98.61%, mIoU = 97.26%, and nice1 = 0.78.
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References
Ballard DH (1987) Generalizing the Hough transform to detect arbitrary shapes. In: Readings in computer vision. Elsevier, pp 714–725
Bendale A, Nigam A, Prakash S, Gupta P (2012) Iris segmentation using improved Hough transform. In: International Conference on Intelligent Computing. Springer, pp 408–415
Bezerra CS, Laroca R, Lucio DR, Severo E, Oliveira LF, Britto AS, Menotti D (2018) Robust iris seg- mentation based on fully convolutional networks and generative adversarial networks. In: 2018 31st SIB- GRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, pp 281–288
Bozomitu RG, Pasarica A, Tarniceriu D, Rotariu C (2019) Development of an eye tracking-based human-computer interface for real-time applications. Sensors 19(16):3630
Chollet F (2017) Xception: Deep learning with depth wise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)
Demos S (2020) Personal electronic device for performing multimodal imaging for non-contact identification of multiple biometric traits. US Patent 10,599,932
Fierrez J, Ortega-Garcia J, Toledano DT, Gonzalez-Rodriguez J (2007) Biosec baseline corpus: A multimodal bio- metric database. Pattern Recogn 40(4):1389–1392
Fitzgibbon A, Pilu M, Fisher RB (1999) Direct least square fitting of ellipses. IEEE Trans Pattern Anal Mach Intell 21(5):476–480
Gangwar A, Joshi A, Singh A, Alonso-Fernandez F, Bigun J (2016) Irisseg: A fast and robust iris segmentation framework for non-ideal iris images. In: 2016 International Conference on Biometrics (ICB). IEEE, pp 1–8
Goodfellow I (2016) Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160
Hapsari R, Utoyo M, Rulaningtyas R, Suprajitno H (2020) Iris segmentation using Hough transform method and fuzzy c-means method. In: Journal of Physics: Conference Series 1477:022037
Hofbauer H, Jalilian E, Uhl A (2019) Exploiting superior CNN-based iris segmentation for better recognition accuracy. Pattern Recogn Lett 120:17–23
Jalilian E, Uhl A (2017) Iris segmentation using fully convolutional encoder–decoder networks. In: Deep Learning for Biometrics. Springer, pp 133–155
Jeong DS, Hwang JW, Kang BJ, Park KR, Won CS, Park DK, Kim J (2010) A new iris segmentation method for non-ideal iris images. Image Vis Comput 28(2):254–260
Kadry S, Rajinikanth V, Damaševičius R, Taniar D (2021) Retinal vessel segmentation with slime-Mould-optimization based multi-scale-matched-filter. In: 2021 seventh international conference on bio signals, images, and instrumentation (ICBSII). IEEE, pp 1–5
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Kumar A, Passi A (2010) Comparison and combination of iris matchers for reliable personal authentication. Pattern Recogn 43(3):1016–1026
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient- based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Li D, Winfield D, Parkhurst DJ (2005) Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops. IEEE, p 79
Lin G, Milan A, Shen C, Reid I (2017) Refinenet: Multi- path refinement networks for high-resolution semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5168–5177
Liu N, Li H, Zhang M, Liu J, Sun Z, Tan T (2016) Ac- curate iris segmentation in non-cooperative environments using fully convolutional networks. In: 2016 International Conference on Biometrics (ICB). IEEE, pp 1–8
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Maqsood S, Damaševičius R, Maskeliūnas R (2021) Hemorrhage detection based on 3D CNN deep learning framework and feature fusion for evaluating retinal abnormality in diabetic patients. Sensors 21(11):3865
Othman N, Dorizzi B, Garcia-Salicetti S (2016) Osiris: an open source iris recognition software. Pattern Recogn Lett 82:124–131
Proenca H, Alexandre L (2007) The nice.i: Noisy iris challenge evaluation - part i, pp 1–4. https://doi.org/10.1109/BTAS.2007.4401910
Proenca H, Filipe S, Santos R, Oliveira J, Alexandre L (2010) The UBIRIS.v2: A database of visible wave- length images captured on-the-move and at-a-distance. IEEE Trans. PAMI 32(8):1529–1535. https://doi.org/10.1109/TPAMI.2009.66
Rajinikanth V, Kadry S, Damaševičius R, Taniar D, Rauf HT (2021, March) Machine-learning-scheme to detect choroidal-neovascularization in retinal OCT image. In: 2021 seventh international conference on bio signals, images, and instrumentation (ICBSII). IEEE, pp 1–5
Ramasamy LK, Padinjappurathu SG, Kadry S, Damaševičius R (2021) Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier. PeerJ Computer Science 7:e456
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234–241
Ryan WJ, Woodard DL, Duchowski AT, Birch- field ST (2008) Adapting starburst for elliptical iris segmentation. In: 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems. IEEE, pp 1–7
Sankowski W, Grabowski K, Napieralska M, Zubert M, Napieralski A (2010) Reliable algorithm for iris segmentation in eye image. Image Vis Comput 28(2):231–237
Tan T, Sun Z (2005) Casia-irisv3. Chinese Academy of Sciences Institute of Automation, http://www.cbsr.ia.ac.cn/IrisDatabase.htm, Tech. Rep
Tuama AS (2012) Iris image segmentation and recognition. International Journal of Computer Science & Emerging Technologies 3(2):60–65
Uchida K (2001) Biometric identification method and system. US Patent App. 09/775,617
Uhl A, Wild P (2012) Weighted adaptive Hough and ellipsopolar transforms for real-time iris segmentation. In: 2012 5th IAPR international conference on biometrics (ICB). IEEE, pp 283–290
Varkarakis V, Bazrafkan S, Corcoran P (2020) Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets. Neural Netw 121:101–121
Wu X, Zhao L (2019) Study on iris segmentation algorithm based on dense u-net. IEEE Access 7:123959–123968
Yang Y, Shen P, Chen C (2018) A robust iris segmentation using fully convolutional network with dilated convolutions. In: 2018 IEEE International Symposium on Multimedia (ISM). IEEE, pp 9–16
Zhang W, Lu X, Gu Y, Liu Y, Meng X, Li J (2019) A robust iris segmentation scheme based on improved u-net. IEEE Access 7:85082–85089
Zhao Z, Ajay K (2015) An accurate iris segmentation frame- work under relaxed imaging constraints using total variation model. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3828–3836
Zuo J, Ratha NK, Connell JH (2008) A new approach for iris segmentation. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, pp 1–6
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Miron, C., Pasarica, A., Manta, V. et al. Efficient and robust eye images iris segmentation using a lightweight U-net convolutional network. Multimed Tools Appl 81, 14961–14977 (2022). https://doi.org/10.1007/s11042-022-12212-8
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DOI: https://doi.org/10.1007/s11042-022-12212-8