Iris Anti-Spoofing Solution for Mobile Biometric Applications

被引：1

作者：

Odinokikh G. ^{[1
]}

Efimov I. ^{[2
]}

Solomatin I. ^{[2
]}

Korobkin M. ^{[3
]}

Matveev I. ^{[1
]}

机构：

[1] Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, Moscow

[2] Moscow Institute of Physics and Technology, Dolgoprudny

[3] National Research University of Electronic Technology, Zelenograd

来源：

Pattern Recognition and Image Analysis | 2018年 / 28卷 / 04期

关键词：

anti-spoofing; iris recognition; machine learning; mobile biometrics; neural networks;

D O I：

10.1134/S1054661818040259

中图分类号：

学科分类号：

摘要：

The ability to provide reliable protection against counterfeiting is one of the key requirements for a biometric security system. Iris recognition as the technology emerging on mobile market is assumed to handle various types of spoof attacks to prevent compromise of the user’s personal data. A method of iris anti-spoofing is proposed in this work. It is based on applying of convolutional neural network and capable to work in real-time on the mobile device with highly limited computational resources. Classification of iris sample for spoof and live is made by a single frame using a pair of images: eye region and normalized iris. The following types of iris spoof samples are considered in this particular work: printed on paper, printed on paper with imposition of a contact lens, printed on paper with application of transparent glue. Testing of the method is performed on the dataset manually collected and containing all the mentioned spoof sample types. The method revealed its high performance in both classification accuracy and processing speed as well as robustness under uncontrollably changing environmental conditions, which are specific and significant when interacting with the mobile device. © 2018, Pleiades Publishing, Ltd.

引用

页码：670 / 675

页数：5

共 26 条

[21]

Yambay D., Becker B., Kohli N., Yadav D., Czajka A., Bowyer K.W., Schuckers S., Singh R., Vatsa M., Noore A., Et al., LivDet iris 2017 — Iris liveness detection competition 2017, Proc. IEEE Int. Joint Conf. on Biometrics, pp. 733-741, (2017)

[22]

Sequeira A., Oliveira H., Monteiro J.C., Monteiro J.P., Cardoso J., MobILive 2014–Mobile iris liveness detection competition, Proc. IEEE Int. Joint Conf. on Biometrics (IJCB 2014), pp. 1-6, (2014)

[23]

Sequeira A., Murari J., Cardoso J., Iris Liveness detection methods in mobile applications, Proc. 9th Int. Conf. on Computer Vision Theory and Applications (VISAPP 2014), 3, pp. 22-33, (2014)

[24]

Howard A.G., Zhu M., Chen B., Kalenichenko D., Wang W., Weyand T., Andreetto M., Adam H., MobileNets: Efficient convolutional neural networks for mobile vision applications, CoRR abs/1704.04861, arXiv:1704.04861, (2017)

[25]

Galbally J., Gomez-Barrero M., A review of iris anti–spoofing, Proc. 2016 4th Int. Conf. on Biometrics and Forensics (IWBF), pp. 1-6, (2016)

[26]

Li H., Sun Z., Zhang M., Wang L., Xiao L., Tan T., A brief survey on recent progress in iris recognition, Biometric Recognition: Proc. 9Th Chinese Conference, 88, (2014)

← 1 2 3 →