More Web Proxy on the site http://driver.im/

research-article

Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets

Authors:

Viktor Varkarakis,

Shabab Bazrafkan,

Peter CorcoranAuthors Info & Claims

Volume 121, Issue C

Pages 101 - 121

https://doi.org/10.1016/j.neunet.2019.07.020

Published: 01 January 2020 Publication History

Abstract

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favourably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets.

References

[1]

Abhyankar A., Hornak L., Schuckers S., Off-angle iris recognition using bi-orthogonal wavelet network system, in: Null, 2005, pp. 239–244.

[2]

Abhyankar A., Schuckers S., Active shape models for effective iris segmentation, in: Biometric technology for human identification III, Vol. 6202, 2006, p. 62020H.

[3]

Ackerman E., Google gets in your face: Google glass offers a slightly augmented version of reality, IEEE Spectrum 50 (1) (2013) 26–29,.

[4]

Arsalan M., Hong H.G., Naqvi R.A., Lee M.B., Kim M.C., Kim D.S., et al., Deep learning-based iris segmentation for iris recognition in visible light environment, Symmetry 9 (11) (2017) 263.

[5]

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) (2018) 1501.

[6]

Bakir A., Chesler G., Torriente M. de la, Using touch ID for local authentication. Internet of things with swift for IOS, 2016.

[7]

Bazrafkan S., Thavalengal S., Corcoran P., An end to end deep neural network for iris segmentation in unconstrained scenarios, Neural Networks 106 (2018) 79–95,.

Digital Library

[8]

Bhorkar G., A survey of augmented reality navigation, Foundations and Trends® in Human–Computer Interaction 8 (2–3) (2017) 73–272,.

Digital Library

[9]

Bowyer K.W., Hollingsworth K., Flynn P.J., Image understanding for iris biometrics: A survey, Computer Vision and Image Understanding 110 (2) (2008) 281–307.

[10]

Bowyer K.W., Hollingsworth K.P., Flynn P.J., A survey of iris biometrics research: 2008–2010, in: Handbook of iris recognition, Vol. 1, Springer, 2013, pp. 15–54.

[11]

Broussard R.P., Ives R.W., Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation, in: Computational intelligence in biometrics: Theory, algorithms, and applications, 2009. CIB 2009. IEEE workshop on, 2009, pp. 46–51.

[12]

CASIA Iris Image Database (2019). (n.d.) Retrieved from http://biometrics.idealtest.org/.

[13]

Cave, A. (2015). Why Google Glass Flopped. Retrieved from http://www.forbes.com/sites/andrewcave/2015/01/20/a-failure-of-leadership-or-design-why-google-glass-flopped/#24c650d3556a.

[14]

Chan P., Halevi T., Memon N., Glass OTP: Secure and convenient user authentication on google glass, in: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics, 2015,.

[15]

Chauhan J., Asghar H.J., Kâafar M.A., Mahanti A., Gesture-based continuous authentication for wearable devices: the google glass Case, in: 14th international conference on applied cryptography and network security, 2016.

[16]

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. ArXiv Preprint ArXiv:1412.7062.

[17]

Chen L.-C., Papandreou G., Kokkinos I., Murphy K., Yuille A.L., Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4) (2018) 834–848.

[18]

Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. ArXiv Preprint ArXiv:1706.05587.

[19]

Chen L.-C., Zhu Y., Papandreou G., Schroff F., Adam H., Encoder–decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European conference on computer vision (ECCV), 2018, pp. 801–818.

Digital Library

[20]

Cherapau I., Muslukhov I., Arachchilage N.A.G., Beznosov K., On the impact of touch ID on iphone passcodes, in: Symposium on usable privacy and security (SOUPS), 2015, pp. 257–276.

[21]

Ching K.W., Singh M.M., Wearable technology devices security and privacy vulnerability analysis, International Journal of Network Security & Its Applications (2016),.

[22]

Cognard, Timothée E., Goncharov, Alexander, Devaney, Nicholas, Dainty, Chris, & Corcoran, Peter (2018). undefined. (n.d.) A Review of Resolution Losses for AR/VR Foveated Imaging Applications. Ieeexplore.Ieee.Org.

[23]

Corcoran P.M., Biometrics and consumer electronics: A brave new world or the road to dystopia?, Consumer Electronics Magazine IEEE 2 (2) (2013) 22–33.

[24]

Corcoran Peter, The battle for privacy in your pocket [notes from the editor], IEEE Consumer Electronics Magazine 5 (3) (2016) 3–36,.

[25]

Corcoran P.M., A privacy framework for the internet of thing, in: 2016 IEEE 3rd world forum on internet of things, WF-IoT 2016, 2017,.

[26]

Corcoran Peter, Bigioi P., Thavalengal S., Feasibility and design considerations for an iris acquisition system for smartphones, in: IEEE international conference on consumer electronics - Berlin, ICCE-Berlin (Vol. 2015-Febru, IEEE, 2015, pp. 164–167,.

[27]

Corcoran P., Costache C., Biometric technology and smartphones: A consideration of the practicalities of a broad adoption of biometrics and the likely impacts, IEEE Consumer Electronics Magazine 5 (2) (2016) 70–78,.

[28]

Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2018). Autoaugment: Learning augmentation policies from data. ArXiv Preprint ArXiv:1805.09501.

[29]

Darwaish S.F., Moradian E., Rahmani T., Knauer M., Biometric identification on android smartphones, in: Procedia computer science, Vol. 35, 2014, pp. 832–841,.

[30]

Daugman J., New methods in iris recognition, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37 (5) (2007) 1167–1175.

[31]

Daugman J., How iris recognition works, in: The essential guide to image processing, Elsevier, 2009, pp. 715–739.

[32]

De Luca A., Hang A., von Zezschwitz E., Hussmann H., I feel like i’m taking selfies all day!, in: Proceedings of the 33rd annual ACM conference on human factors in computing systems - CHI ’15, 2015,.

Digital Library

[33]

Dorairaj V., Schmid N.A., Fahmy G., Performance evaluation of non-ideal iris based recognition system implementing global ICA encoding, in: Image processing, 2005. ICIP 2005. IEEE international conference on, Vol. 3, 2005, III–285.

[34]

Elise, B. (2014). Google Glass a Game-changing Application in the Realm of Cultural Tourism. Business Wire. Retrieved from http://search.proquest.com/docview/1635054301?accountid=14744%5Cn http://fama.us.es/search*spi/i?SEARCH=%5Cn http://pibserver.us.es/gtb/usuario_acceso.php?centro=USEG&centro=%24USEG&d=1.

[35]

Erbilek M., Da Costa-Abreu M.C., Fairhurst M., Optimal configuration strategies for iris recognition processing, 2012.

[36]

Fox B., Felkey B., Potential uses of google glass in the pharmacy, Hospital Pharmacy 48 (9) (2013) 783–784,.

[37]

Gangwar A., Joshi A., Singh A., Alonso-Fernandez F., Bigun J., IrisSeg: A fast and robust iris segmentation framework for non-ideal iris images, in: 2016 international conference on biometrics (ICB), IEEE, 2016, pp. 1–8.

[38]

Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., & Garcia-Rodriguez, J. 2017. A review on deep learning techniques applied to semantic segmentation. ArXiv Preprint ArXiv:1704.06857.

[39]

Goode A., Bring your own finger – how mobile is bringing biometrics to consumers, Biometric Technology Today 2014 (5) (2014) 5–9,.

[40]

Goodfellow I., Bengio Y., Courville A., Deep learning, MIT press, 2016.

Digital Library

[41]

Hammal Z., Massot C., Bedoya G., Caplier A., Singh S., Singh M., Apte C., Perner P. (Eds.), Eyes segmentation applied to gaze direction and vigilance estimation BT - pattern recognition and image analysis, Springer Berlin Heidelberg, Berlin, Heidelberg, 2005, pp. 236–246.

[42]

Hayes A., My journey into glass: Talking about google glass with stakeholders in the glass explorer program, IEEE Consumer Electronics Magazine 5 (1) (2016) 102–105,.

[43]

He X., Shi P., A novel iris segmentation method for hand-held capture device, in: International conference on biometrics, Springer, 2006, pp. 479–485.

[44]

Hofbauer Heinz, Alonso-Fernandez F., Bigun J., Uhl A., Experimental analysis regarding the influence of iris segmentation on the recognition rate, IET Biometrics 5 (3) (2016) 200–211.

[45]

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, 2014, pp. 527–532,.

Digital Library

[46]

Huang Y.-P., Luo S.-W., Chen E.-Y., An efficient iris recognition system, in: Machine learning and cybernetics, 2002. Proceedings. 2002 international conference on, Vol. 1, IEEE, 2002, pp. 450–454.

[47]

Jalilian E., Uhl A., Kwitt R., Domain adaptation for CNN based Iris segmentation. BIOSIG 2017, 2017.

[48]

Jan F., Segmentation and localization schemes for non-ideal iris biometric systems, Signal Processing 133 (2017) 192–212.

[49]

Jiang Z., Yuan Y., Wang Q., Contour-aware network for semantic segmentation via adaptive depth, Neurocomputing 284 (2018) 27–35.

[50]

Jillela R., Ross A.A., Methods for iris segmentation, in: Handbook of Iris recognition, Springer, 2013, pp. 239–279.

[51]

Khan T.M., Khan M.A., Malik S.A., Khan S.A., Bashir T., Dar A.H., Automatic localization of pupil using eccentricity and iris using gradient based method, Optics and Lasers in Engineering 49 (2) (2011) 177–187.

[52]

Koh J., Govindaraju V., Chaudhary V., A robust iris localization method using an active contour model and hough transform, in: Pattern recognition (ICPR), 2010 20th international conference on, 2010, pp. 2852–2856.

[53]

Kress B., Saeedi E., Brac-de-la Perriere V., The segmentation of the HMD market: optics for smart glasses, smart eyewear, AR and VR headsets, in: Photonics applications for aviation, aerospace, commercial, and harsh environments V, Vol. 9202, 2014, p. 92020D,.

[54]

Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, in: Advances in neural information processing systems, 2012, pp. 1097–1105.

Digital Library

[55]

Lakra A., Tripathi P., Keshari R., Vatsa M., Singh R., Segdensenet: Iris segmentation for pre-and-post Cataract surgery, in: 2018 24th international conference on pattern recognition (ICPR), IEEE, 2018, pp. 3150–3155.

[56]

Lateef F., Ruichek Y., Survey on semantic segmentation using deep learning techniques, Neurocomputing (2019).

[57]

Lemley J., Bazrafkan S., Corcoran P., Smart augmentation learning an optimal data augmentation strategy, IEEE Access 5 (2017) 5858–5869,.

[58]

Li X., Modeling intra-class variation for nonideal iris recognition, in: International conference on biometrics, 2006, pp. 419–427.

[59]

Lili P., Mei X., The algorithm of iris image preprocessing, in: Automatic identification advanced technologies, 2005. Fourth IEEE workshop on, IEEE, 2005, pp. 134–138.

[60]

Linao M., The present and future of VR/AR: Applications in education, gaming, commerce, and industry, 2016.

[61]

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

[62]

Liu Y., Yuan S., Zhu X., Cui Q., A practical iris acquisition system and a fast edges locating algorithm in iris recognition, in: IEEE instrumentation and measurement technology conference proceedings, Vol. 1, 2003, pp. 166–169.

[63]

Mann S., Fundamental issues in mediated reality, wearcomp, and camera-based augmented reality, in: Fundamentals of wearable computers and augmented reality, Lawrence Erlbaum Associates, Inc., 2001, pp. 295–328,.

[64]

Mann S., Continuous lifelong capture of personal experience with EyeTap, in: Proceedings of the 1st ACM workshop on continuous archival and retrieval of personal experiences - CARPE’04, 2004, pp. 1–21,.

Digital Library

[65]

Mann S., Steve mann: My augmented life, IEEE Spectrum (2013) 1–6.

[66]

Mann S., Fung J., Eyetap devices for augmented, deliberately diminished, or otherwise altered visual perception of rigid planar patches of real-world scenes, Presence: Teleoperators & Virtual Environments 11 (2) (2002) 158–175,.

Digital Library

[67]

MIRLIN (2019). (n.d.) Retrieved from https://www.fotonation.com/products/biometrics/iris-recognition/.

[68]

Muensterer O.J., Lacher M., Zoeller C., Bronstein M., Kübler J., Google glass in pediatric surgery: An exploratory study, International Journal of Surgery 12 (4) (2014) 281–289,.

[69]

Othman N., Dorizzi B., Garcia-Salicetti S., OSIRIS: An open source iris recognition software, Pattern Recognition Letters 82 (2016) 124–131.

[70]

Peng G., Zhou G., Nguyen D.T., Qi X., Yang Q., Wang S., Continuous authentication with touch behavioral biometrics and voice on wearable glasses, IEEE Transactions on Human-Machine Systems (2017),.

[71]

Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. ArXiv Preprint ArXiv:1712.04621.

[72]

Prabhakar S., Pankanti S., Jain A.K., Biometric recognition: security and privacy concerns, IEEE Security & Privacy 1 (2) (2003) 33–42,.

Digital Library

[73]

Proenca H., Iris recognition: On the segmentation of degraded images acquired in the visible wavelength, IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (8) (2010) 1502–1516.

[74]

Proenca H., Filipe S., Santos R., Oliveira J., Alexandre L.A., The ubiris. v2: A database of visible wavelength iris images captured on-the-move and at-a-distance, IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (8) (2010) 1529–1535.

[75]

Proença H., Alexandre L.A., Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage, Image and Vision Computing 28 (1) (2010) 202–206.

Digital Library

[76]

Quinn, G. W., Grother, P. J., Ngan, M. L., & Matey, J. R. (2013). IREX IV: part 1, evaluation of iris identification algorithms.

[77]

Radman A., Zainal N., Suandi S.A., Automated segmentation of iris images acquired in an unconstrained environment using HOG-svm and growcut, Digital Signal Processing 64 (2017) 60–70.

[78]

Rakshit S., Novel methods for accurate human Iris recognition, University of Bath, 2007.

[79]

Ring T., Spoofing: are the hackers beating biometrics?, Biometric Technology Today 2015 (7) (2015) 5–9,.

[80]

Rompapas D.C., Rovira A., Ikeda S., Plopski A., Taketomi T., Sandor C., et al., Eyear: Refocusable augmented reality content through eye measurements, in: Adjunct proceedings of the 2016 IEEE international symposium on mixed and augmented reality, ISMAR-Adjunct 2016, 2017,.

[81]

Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., et al., Imagenet large scale visual recognition challenge, International Journal of Computer Vision 115 (3) (2015) 211–252.

Digital Library

[82]

Salamon J., Bello J.P., Deep convolutional neural networks and data augmentation for environmental sound classification, IEEE Signal Processing Letters 24 (3) (2017) 279–283.

[83]

Samangouei P., Patel V.M., Chellappa R., Facial attributes for active authentication on mobile devices, Image and Vision Computing (2017),.

Digital Library

[84]

Schlüter J., Grill T., Exploring data augmentation for improved singing voice detection with neural networks, in: ISMIR, 2015, pp. 121–126.

[85]

Schreinemacher M.H., Graafland M., Schijven M.P., Google glass in surgery, Surgical Innovation (2014),.

[86]

Shah S., Ross A., Iris segmentation using geodesic active contours, IEEE Transactions on Information Forensics and Security 4 (4) (2009) 824–836.

Digital Library

[87]

Shejin Thavalengal, Corcoran P., User authentication on smartphones: Focusing on iris biometrics, IEEE Consumer Electronics Magazine 5 (2) (2016) 87–93,.

[88]

Shijie J., Ping W., Peiyi J., Siping H., Research on data augmentation for image classification based on convolution neural networks, in: 2017 Chinese automation congress (CAC), IEEE, 2017, pp. 4165–4170.

[89]

Starner T., Mann S., Rhodes B., Levine J., Healey J., Kirsch D., et al., Augmented reality through wearable computing, Presence: Teleoperators and Virtual Environments 6 (4) (1997) 386–398,.

Digital Library

[90]

Tan T., He Z., Sun Z., Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition, Image and Vision Computing 28 (2) (2010) 223–230.

[91]

Tan C.-W., Kumar A., Unified framework for automated iris segmentation using distantly acquired face images, IEEE Transactions on Image Processing 21 (9) (2012) 4068–4079.

[92]

Tan C.-W., Kumar A., Towards online iris and periocular recognition under relaxed imaging constraints, IEEE Transactions on Image Processing 22 (10) (2013) 3751–3765.

[93]

Tang F., Aimone C., Fung J., Marjan A., Mann S., Seeing eye to eye: A shared mediated reality using eyetap devices and the videoorbits gyroscopic head tracker, in: Proceedings - international symposium on mixed and augmented reality, ISMAR 2002, 2002, pp. 267–268,.

[94]

Taylor, L., & Nitschke, G. (2017). Improving deep learning using generic data augmentation. ArXiv Preprint ArXiv:1708.06020.

[95]

Thavalengal Shejin, Andorko I., Drimbarean A., Bigioi P., Corcoran P., Proof-of-concept and evaluation of a dual function visible/NIR camera for iris authentication in smartphones, IEEE Transactions on Consumer Electronics 61 (2) (2015) 137–143,.

Digital Library

[96]

Thavalengal S., Bigioi P., Corcoran P., Evaluation of combined visible/NIR camera for iris authentication on smartphones, in: 2015 IEEE conference on computer vision and pattern recognition workshops (CVPRW), 2015, pp. 42–49,.

[97]

Thavalengal S., Bigioi P., Corcoran P., Iris authentication in handheld devices - considerations for constraint-free acquisition, IEEE Transactions on Consumer Electronics 61 (2) (2015) 245–253,.

Digital Library

[98]

Thavalengal Shejin, Bigioi P., Corcoran P., Efficient segmentation for multi-frame iris acquisition on smartphones, in: 2016 IEEE international conference on consumer electronics (ICCE) (2016 ICCE), 2016, pp. 202–203.

[99]

Timekeeper Shejin, The promise of augmented reality, The Economist (2017).

[100]

Tipton Stephen J., White II Daniel J., Sershon Christopher, Choi Young B., Ios security and privacy: Authentication methods, permissions, and potential pitfalls with touch id, International Journal of Computer and Information Technology 3 (3) (2014) 482–489.

[101]

Varkarakis V., Bazrafkan S., Corcoran P., A deep learning approach to segmentation of distorted iris regions in head-mounted displays, in: 2018 IEEE games, entertainment, media conference (GEM), IEEE, 2018, pp. 1–9.

[102]

Vazquez-Fernandez E., Gonzalez-Jimenez D., Face recognition for authentication on mobile devices, Image and Vision Computing (2016),.

Digital Library

[103]

Wang Q., Gao J., Yuan Y., Embedding structured contour and location prior in siamesed fully convolutional networks for road detection, IEEE Transactions on Intelligent Transportation Systems 19 (1) (2018) 230–241.

[104]

WaveLab (2019). (n.d.) No Title.

[105]

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

[106]

Yadav D.K., Ionascu B., Ongole S.V.K., Roy A., Memon N., Design and analysis of shoulder surfing resistant pin based authentication mechanisms on google glass, in: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics, 2015,.

[107]

Zhao Z., Ajay K., An accurate iris segmentation framework under relaxed imaging constraints using total variation model, in: Proceedings of the IEEE international conference on computer vision, 2015, pp. 3828–3836.

Cited By

Banerjee AGhosh CMandal S(2022)Analysis of V-Net Architecture for Iris Segmentation in Unconstrained ScenariosSN Computer Science10.1007/s42979-022-01113-03:3Online publication date: 3-Apr-2022
https://dl.acm.org/doi/10.1007/s42979-022-01113-0
Miron CPasarica AManta VTimofte R(2022)Efficient and robust eye images iris segmentation using a lightweight U-net convolutional networkMultimedia Tools and Applications10.1007/s11042-022-12212-881:11(14961-14977)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s11042-022-12212-8
Malgheet JManshor NAffendey L(2021)Iris Recognition Development TechniquesComplexity10.1155/2021/66412472021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6641247
Show More Cited By

Index Terms

Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets

Index terms have been assigned to the content through auto-classification.

Recommendations

An end to end Deep Neural Network for iris segmentation in unconstrained scenarios
Abstract
With the increasing imaging and processing capabilities of today’s mobile devices, user authentication using iris biometrics has become feasible. However, as the acquisition conditions become more unconstrained and as image quality is ...
Biometric iris recognition using radial basis function neural network
Abstract
The consistent and efficient method for the identification of biometrics is the iris recognition in view of the fact that it has richness in texture information. A good number of features performed in the past are built on handcrafted features. ...
Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition

This paper describes the winning algorithm we submitted to the recent NICE.I iris recognition contest. Efficient and robust segmentation of noisy iris images is one of the bottlenecks for non-cooperative iris recognition. To address this problem, a novel ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Neural Networks

Neural Networks Volume 121, Issue C

Jan 2020

536 pages

ISSN:0893-6080

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 January 2020

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Banerjee AGhosh CMandal S(2022)Analysis of V-Net Architecture for Iris Segmentation in Unconstrained ScenariosSN Computer Science10.1007/s42979-022-01113-03:3Online publication date: 3-Apr-2022
https://dl.acm.org/doi/10.1007/s42979-022-01113-0
Miron CPasarica AManta VTimofte R(2022)Efficient and robust eye images iris segmentation using a lightweight U-net convolutional networkMultimedia Tools and Applications10.1007/s11042-022-12212-881:11(14961-14977)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s11042-022-12212-8
Malgheet JManshor NAffendey L(2021)Iris Recognition Development TechniquesComplexity10.1155/2021/66412472021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6641247
Wang CWang YZhang KMuhammad JLu TZhang QTian QHe ZSun ZZhang YLiu TYang WWu DLiu YZhou RWu HZhang HWang JWang JXiong WShi XZeng SLi PSun HWang JZhang JWang QWu HZhang XLi HChen YChen LZhang MSun YZhou ZBoutros FDamer NKuijper ATapia JValenzuela ABusch CGupta GRaja KWu XLi XYang JJing HWang XKong BYin YSong QLyu SHu SPremk LVitek MŠtruc VPeer PKhiarak JJaryani FNasab SMoafinejad SAmini YNoshad M(2021)NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization2021 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB52358.2021.9484336(1-10)Online publication date: 4-Aug-2021
https://dl.acm.org/doi/10.1109/IJCB52358.2021.9484336
Dargan SKumar M(2020)A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalitiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.113114143:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.eswa.2019.113114

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents