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Editor-in-Chief

Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2018-18-3-457-461
FACE RECOGNITION SYSTEM FOR PAYMENT PROCESS ON MOBILE DEVICES AND WEB-APPLICATIONS
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Article in Russian
For citation: Ivanko D.V. Face recognition system for payment process on mobile devices and web-applications. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 3, pp. 457–461 (in Russian). doi: 10.17586/2226-1494-2018-18-3-457-461
Abstract
For citation: Ivanko D.V. Face recognition system for payment process on mobile devices and web-applications. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 3, pp. 457–461 (in Russian). doi: 10.17586/2226-1494-2018-18-3-457-461
Abstract
The paper deals with the problem of users' identity authentication during payment transactions with the use of mobile devices and web applications. Standard methods of users' identification are considered at performing a payment transaction. The subjects of discussion are the main criteria for the effectiveness of user identification systems in mobile devices and web applications, such as the identification accuracy of the modern systems, time and computational costs, the ability to distribute computations and user convenience. Particular attention is paid to computational and time costs, as they are the most significant for users who make use of practically applicable client-server mobile and web applications. The advantages and disadvantages of face recognition systems application for users' identification and verification are pointed out. Each system element participating in secure banking transaction is described in the course of the payment transaction. A new client-server model is presented for interaction of the face recognition system for security assurance while shopping with the use of mobile devices or web applications. Experimental estimates of the face recognition systems effectiveness are also given. The developed architecture gave the possibility to reduce the time spent by the client for the transaction by an average of 47%, compared with application of standard user authentication tools.
Keywords: face recognition systems, payment systems, client-server applications, web-applications, mobile devices and software
Acknowledgements. This paper was supported by the Ministry of Education and Science of the Russian Federation, state project No. 8.9957.2017 / 5.2.
References
Acknowledgements. This paper was supported by the Ministry of Education and Science of the Russian Federation, state project No. 8.9957.2017 / 5.2.
References
1. Gunther M., Costa-Pazo A., Ding C. et al. The 2013 face recognition evaluation in mobile environment. Proc. Int. Conf. on Biometrics. Madrid, Spain, 2013.doi: 10.1109/ICB.2013.6613024
2. Labeled Faces in the Wild: Results. Available at: http://vis-www.cs.umass.edu/lfw/results.html (accessed 20.03.2018).
3. Vazquez-Fernandez E., Gonzalez-Jimenez D. Face recognition for authentication on mobile devices. Image and Vision Computing, 2016, vol. 55, pp. 31–33. doi: 10.1016/j.imavis.2016.03.018
4. Casti S., Sorrentino F., Spano L.D., Scateni R. Click and share: A face recognition tool for the mobile community. Proc. Int. Conf. on Image Processing, ICIP, 2014, pp. 1952–1956. doi: 10.1109/ICIP.2014.7025391
5. Srirama S.N., Paniagua C., Flores H. Social group formation with mobile cloud services. Service Oriented Computing and Applications, 2012, vol. 6, no. 4, pp. 351–362. doi: 10.1007/s11761-012-0111-5
6. Lochner S.A. Saving face: regulating law enforcement’s use of mobile facial recognition technology and Iris scans. Arizona Law Review, 2013, vol. 55, no. 1, pp. 201–233.
7. Arrivals Smart Gate. Available at: http://www.homeaffairs.gov.au/Trav/Ente/Goin/Arrival/Smartgateor-ePassport (accessed 20.03.2018).
8. Kukharev G.A., Kamenskaya E.I., Matveev Y.N., Shchegoleva N.L. Methods for Face Image Processing and Recognition in Biometric Applications. Ed. M.V. Khitrov. St. Petersburg, Politekhnika Publ., 2013, 388 p. (In Russian)
9. Ivanko D.V. Modeling of face recognition systems using mnemonic description of the model. Computer Tools in Education, 2016, no. 1, pp. 17–23.(In Russian)
10. Classification: Accuracy. Available at: https://developers.google.com/machine-learning/crash-course/classification/accuracy (accessed 20.03.2018).
11. Learned-Miller E., Huang G.B., RoyChowdhury A., Li H., Hua G. Labeled faces in the wild: a survey. In Advances in Face Detection and Facial Image Analysis. Springer, 2016, pp. 189–248. doi: 10.1007/978-3-319-25958-1_8
Every second counts: why the speed of the page should be your next focus. Available at: http://thewall.by/kazhdaya-sekunda-na-schetu-pochemu-skorost-stranicy-dolzhna-stat-vashim-sleduyushhim-centrom-vnimaniya (accessed 20.03.2018