Multifactor authentication using a kinect sensor
Keywords:
Kinect, multifactor, authentication
Abstract
In this paper a multifactor authentication system by using Kinect sensor and computer equipment was developed. It was used the C# language for coding with the development environment and tools provided by the manufacturer for Windows operating system, to choose a combination of authentication methods to reduce the ability of a non-authorized user to be eligible to access a certain place or system. Five methods were chosen to obtain the multifactor authentication, covering the three categories of authentication methods: Information keys, physical keys and biometric keys, based respectively in something the person knows, something the person has and something the person is. A reliable, robust and easy to use authentication system was achieved, favoring the reliability and reducing the complexity of each of the individual methods. It proved to be possible to develop a multifactor authentication system with Kinect sensor.Downloads
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References
[1] E. A. Fisch and G. B. White, Secure computers and networks. Analysis, design and implementation. CRC Press, 1999, pp. 1-4, 53-67.
[2] M. Burnett, Perfect passwords: Selection, protection, authentication, Rockland: Syngress Publishing, 2006, pp. 76, 131, 133, 134.
[3] J. Killmeyer, Information security architecture. An integrated approach to security in the organization, Auerbach: CRC Press, 2006, pp. 101, 114-116.
[4] D. Catuhe, Programming with the Kinect for Windows Software Development Kit. Redmond, Washington: Pearson Education, 2012, pp. 3-5, 27, 32.
[5] J. Abhijit, Kinect for Windows SDK Programming Guide, Mumbai: Packt Publishing, 2012, pp. 7-18, 19, 21, 37-39, 47-53.
[6] J. Přinosil, K. Říha, and F. Dongmei, “Kinect Based Automated Access Control Systems,” Department of Telecommunications, Brno University of Technology. Department of Automation, University of Science and Technology, Beijing Latest Trends in Information Technology. Nov. 2012.
[7] S. McSheehy, and E. Cowley, Home Security Prototype Device, University of Massachusetts Lowell. May. 2012.
[8] M. Martínez-Zarzuela, F.J. Díaz-Pernas, A. Tejero de Pablos, F. Perozo-Rondón, M. Antón-Rodríguez, and D. González-Ortega, “Monitorización del cuerpo humano en 3D mediante tecnología Kinect,” SAAEI, 747-752. 2011.
[9] M. Afizi, M. Shukran, M. Suhaili, and B. Ariffin, “Kinect-based Gesture Password Recognition,” Faculty of Science and Defence Technology, Universiti Pertahanan Nasional Malaysia, Australian Journal of Basic and Applied Sciences, vol. 6 no. 8, pp. 492-499, 2012.
[10] L. Li, “Gesture-based User Authentication with Kinect,” Boston University, Department of Electrical and Computer Engineering, Technical Report No. ECE-2013-2. April 7, 2013.
[11] J. Wu, K. Janus, and I. Prakash, “The Value of Multiple Viewpoints in Gesture-Based User Authentication,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on. IEEE, 2014.
[12] S. J. Fluckiger, Security with Visual Understanding: Kinect Human Recognition Capabilities Applied in a Home Security System, The University of Texas at Austin. May. 2012.
[13] Araujo, M. Ricardo, G. Graña, and V. Andersson, “Towards skeleton biometric identification using the microsoft kinect sensor,” Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, 2013.
[14] J. Wu, K. Janus, and I. Prakash, “Dynamic time warping for gesture-based user identification and authentication with Kinect,” Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013.
[15] B. Dikovski, G. Madjarov, and D. Gjorgjevikj, “Evaluation of different feature sets for gait recognition using skeletal data from Kinect,” Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on. IEEE, 2014.
[16] E. Hayashi, M. Maas, and J. I. Hong, “Wave to me: user identification using body lengths and natural gestures,” Proceedings of the 32nd annual ACM Conference on Human Factors in Computing Systems. ACM, 2014.
[2] M. Burnett, Perfect passwords: Selection, protection, authentication, Rockland: Syngress Publishing, 2006, pp. 76, 131, 133, 134.
[3] J. Killmeyer, Information security architecture. An integrated approach to security in the organization, Auerbach: CRC Press, 2006, pp. 101, 114-116.
[4] D. Catuhe, Programming with the Kinect for Windows Software Development Kit. Redmond, Washington: Pearson Education, 2012, pp. 3-5, 27, 32.
[5] J. Abhijit, Kinect for Windows SDK Programming Guide, Mumbai: Packt Publishing, 2012, pp. 7-18, 19, 21, 37-39, 47-53.
[6] J. Přinosil, K. Říha, and F. Dongmei, “Kinect Based Automated Access Control Systems,” Department of Telecommunications, Brno University of Technology. Department of Automation, University of Science and Technology, Beijing Latest Trends in Information Technology. Nov. 2012.
[7] S. McSheehy, and E. Cowley, Home Security Prototype Device, University of Massachusetts Lowell. May. 2012.
[8] M. Martínez-Zarzuela, F.J. Díaz-Pernas, A. Tejero de Pablos, F. Perozo-Rondón, M. Antón-Rodríguez, and D. González-Ortega, “Monitorización del cuerpo humano en 3D mediante tecnología Kinect,” SAAEI, 747-752. 2011.
[9] M. Afizi, M. Shukran, M. Suhaili, and B. Ariffin, “Kinect-based Gesture Password Recognition,” Faculty of Science and Defence Technology, Universiti Pertahanan Nasional Malaysia, Australian Journal of Basic and Applied Sciences, vol. 6 no. 8, pp. 492-499, 2012.
[10] L. Li, “Gesture-based User Authentication with Kinect,” Boston University, Department of Electrical and Computer Engineering, Technical Report No. ECE-2013-2. April 7, 2013.
[11] J. Wu, K. Janus, and I. Prakash, “The Value of Multiple Viewpoints in Gesture-Based User Authentication,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on. IEEE, 2014.
[12] S. J. Fluckiger, Security with Visual Understanding: Kinect Human Recognition Capabilities Applied in a Home Security System, The University of Texas at Austin. May. 2012.
[13] Araujo, M. Ricardo, G. Graña, and V. Andersson, “Towards skeleton biometric identification using the microsoft kinect sensor,” Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, 2013.
[14] J. Wu, K. Janus, and I. Prakash, “Dynamic time warping for gesture-based user identification and authentication with Kinect,” Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013.
[15] B. Dikovski, G. Madjarov, and D. Gjorgjevikj, “Evaluation of different feature sets for gait recognition using skeletal data from Kinect,” Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on. IEEE, 2014.
[16] E. Hayashi, M. Maas, and J. I. Hong, “Wave to me: user identification using body lengths and natural gestures,” Proceedings of the 32nd annual ACM Conference on Human Factors in Computing Systems. ACM, 2014.
Published
2016-04-04
How to Cite
Moumtadi, F., & García-Vázquez, L. (2016). Multifactor authentication using a kinect sensor. ITECKNE, 13(1), 23-35. https://doi.org/https://doi.org/10.15332/iteckne.v13i1.1379
Issue
Section
Research and Innovation Articles
