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Tytuł artykułu

The overview of trends and challenges in mobile biometrics

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Currently, various biometric modalities are used to perform human identification or user verification. Although the research results are promising, the constant development of biometric systems is needed. Recently, biometric systems are also implemented for mobile devices, services and applications. In this article, the review of current trends in mobile biometrics is discussed. The paper also describes the most challenging aspects like aging, template protection or wide users’ acceptance. Finally, palmprints are described as the trait that may give promising results and could be implemented widely in mobile biometrics.
Rocznik
Strony
173--185
Opis fizyczny
Bibliogr. 39 poz., rys., tab.
Twórcy
  • Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, Bydgoszcz, Poland
autor
  • Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, Bydgoszcz, Poland
autor
  • Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, Bydgoszcz, Poland
Bibliografia
  • [1] https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx.
  • [2] Siddique K., Akhtar Z., Kim Y., Biometrics vs passwords: a modern version of the tortoise and the hare, Comput. Fraud Secur. 2017, 2017, 1, 13-17.
  • [3] Unar J.A., Seng W.C., Abbasi A., A review of biometric technology along with trends and prospects, Pattern Recognit. 2014, 47, 8, Aug., 2673-2688.
  • [4] Jain A.K., Nandakumar K., Ross A., 50 years of biometric research: Accomplishments, challenges, and opportunities, Pattern Recognit. Lett. 2016, 79, Aug., 80-105.
  • [5] Zhang D., Kong W.-K., You J., Wong M., Online palmprint identification, IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25, 9, 1041-105.
  • [6] Zhao W., Chellappa R., Phillips P.J., Rosenfeld A., Face recognition: A literature survey, ACM Comput. Surv. CSUR 2003, 35, 4, 399-458.
  • [7] Choras M., Ear biometrics based on geometrical feature extraction, ELCVIA Electron. Lett.Comput. Vis. Image Anal. 2005, 5, 3, 84-95.
  • [8] Nappi M., Ricciardi S., Tistarelli M., Real Time 3D Face-Ear Recognition on Mobile Devices: New Scenarios for 3D Biometrics ‘in-the-Wild’, [In:] Human Recognition in Unconstrained Environments, Academic Press, 2017, 55-75.
  • [9] Daugman J., How iris recognition works, IEEE Trans. Circuits Syst. Video Technol. 2004, 14, 1, Jan., 21-30.
  • [10] Hong L., Jain A., Integrating faces and fingerprints for personal identification, IEEE Trans. Pattern Anal. Mach. Intell. 1998, 20, 12, 1295-1307.
  • [11] Nigam A., Tiwari K., Gupta P., Multiple texture information fusion for finger-knuckle-print authentication system, Neurocomputing 2016, 188, May, 190-205.
  • [12] Jillela R.R., Ross A., Segmenting iris images in the visible spectrum with applications in mobile biometrics, Pattern Recognit. Lett., 2015, 57, May, 4-16.
  • [13] Zhang Y., Chen Z., Hui X., Wei T., Fingerprints on Mobile Devices Abusing and Leaking, https://www.blackhat.com/docs/us-15/materials/us-15-Zang-Fingerprints-On-Mobile-Devices-Abusing-And-Leaking-wp.pdf
  • [14] https://srlabs.de/bites/spoofing-fingerprints, Security Research Labs.
  • [15] Lanitis A., A survey of the effects of aging on biometric identity verification, Int. J. Biom. 2009, 2, 1, 34-52.
  • [16] Baker S.E., Bowyer K.W., Flynn P.J., Phillips P.J., Template aging in iris biometrics, [In:] Handbook of Iris Recognition, Springer, 2013, 205-218.
  • [17] Pravallika P., Prasad K.S., SVM classification for fake biometric detection using image quality assessment: Application to iris, face and palm print, Inventive Computation Technologies (ICICT), International Conference on, 2016, 1, 1-6.
  • [18] Bhilare S., Kanhangad V., Chaudhari N., A study on vulnerability and presentation attack detection in palmprint verification system, Pattern Anal. Appl. 2017, Feb.
  • [19] Tresadern P. et al., Mobile biometrics: Combined face and voice verification for a mobile platform, IEEE Pervasive Comput. 2013, 121, 79-87.
  • [20] Wu F., Xu L., Kumari S., Li X., A novel and provably secure biometrics-based three-factor remote authentication scheme for mobile client-server networks, Comput. Electr. Eng. 2015, 45, 274-285, Jul.
  • [21] Bommagani A.S., Valenti M.C., Ross A., A Framework for Secure Cloud-Empowered Mobile Biometrics, Proc. of IEEE Military Communications Conference (MILCOM), Baltimore, MD, 2014, October, 255-261.
  • [22] Lancelot Miltgen C., Popovič A., Oliveira T., Determinants of end-user acceptance of biometrics: Integrating the ‘Big 3’ of technology acceptance with privacy context, Decis. Support Syst., Elsevier, 2013, 56, 103-114, Dec.<10.1016/j.dss.2013.05.010>
  • [23] El-Abed M., Giot R., Hemery B., Rosenberger C., A study of users’ acceptance and satisfaction of biometric systems, in Security Technology (ICCST), 2010 IEEE International Carnahan Conference on, 2010, 170-178.
  • [24] http://biometrics.idealtest.org/index.jsp.
  • [25] http://www4.comp.polyu.edu.hk/~biometrics/.
  • [26] http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm.
  • [27] Fierrez J. et al., BiosecurID: a multimodal biometric database, Pattern Anal. Appl. 2010, 13, May, 2, 235-246.
  • [28] Ross A., Jain A.K., Multimodal biometrics: An overview, Signal Processing Conference, 2004 12th European, 2004, 1221-1224.
  • [29] Taouche C., Batouche M.C., Berkane M., Taleb-Ahmed A., Multimodal biometric systems, Multimedia Computing and Systems (ICMCS), 2014 International Conference on, 2014, pp. 301--308.
  • [30] Gupta G., Dixit M., CBIR on Biometric Application using Hough Transform with DCD, DWT Features and SVM Classification, Image (IN), 2016, 5, 12.
  • [31] Lobiyal D.K., Mohapatra D.P., Nagar A., Sahoo M.N. (eds.), Proceedings of the International Conference on Signal, Networks, Computing, and Systems, vol. 395. Springer India, New Delhi 2017.
  • [32] Jaswal G., Nath R., Kaul A., Texture based palm Print recognition using 2-D Gabor filter and sub space approaches, Signal Processing, Computing and Control (ISPCC), 2015 International Conference on, 2015, 344-349.
  • [33] Jayanthi K., Karthikeyan M., An experimental comparison of features in content based image retrieval system, Computational Intelligence and Computing Research (ICCIC), 2015 IEEE International Conference on, 2015, 1-4.
  • [34] Czajka A., Bulwan P., Biometric verification based on hand thermal images, 2013 International Conference on Biometrics (ICB), 2013, 1-6.
  • [35] Jasiński P., Forczmański F., Combined imaging system for taking facial portraits in visible and thermal spectra, Image Process. Commun. Chall. 2015, 7, 389, 63-71.
  • [36] Killioğlu M., Taşkiran M., Kahraman N., Anti-spoofing in face recognition with liveness detection using pupil tracking, Applied Machine Intelligence and Informatics (SAMI), 2017 IEEE 15th International Symposium on, 2017, 87-92.
  • [37] Smiatacz M., Liveness measurements using optical flow for biometric person authentication, Metrol. Meas. Syst. 2012, 19, 2, Jan.
  • [38] Bounneche M.D., Boubchir L., Bouridane A., Nekhoul B., Ali-Cherif A., Multi-spectral palmprint recognition based on oriented multiscale log-Gabor filters, Neurocomputing 2016, 205, September, 274-286.
  • [39] Sherawat H., Dalal S., Palmprint recognition system using 2-D Gabor and SVM as classifier, IJITR 2016, 4, 3, 3007-3010.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b5b92c4b-d5ac-4e13-a3fc-f8a632823096
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