Abstract
User authentication is becoming a significant factor in the field of modern technology. It is a process that permits a device to confirm the recognition of somebody who interfaces with a system asset. In the world of AI, machine learning is currently one of the leading research fields which is looking into practical implementation. In this paper, we propose a method where the user will enter the given password while the leap motion sensor will compare the behavioral data of the user with an existing dataset. Leap motion controller is a sensor which can recognize 3D movement of hands, fingers, and finger-like object with no contact. For our project, we chose to use dynamic time warping and Naive Bayes classifier algorithm. For the experiment, we captured the leap motion data of eight people and stored them in the database for user authentication and another eight people were used as third-party users. Moreover, as the password is being typed into the keyboard, it is important to restrict the intrusion of malicious third-party users. The proposed system demonstrates about 91% accuracy which rises to 93% in the best-case scenario.
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References
A. Rahman, T. Rahman, N.H. Ghani, S. Hossain, J. Uddin, IoT based patient monitoring system using ECG sensor, in 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka (2019), pp. 378–382
S. Abdulkader, A. Atia, M. Mostafa, Authentication systems: principles and threats. Comput. Inf. Sci. 8(3) (2015)
S. Ahmed, T.M. Tan, A.M. Mondol, Z. Alam, N. Nawal, J. Uddin, Automated toll collection system based on RFID sensor, in International Carnahan Conference on Security Technology, India (2019), pp. 1–3
Cmarsden, 4 reasons why biometric security is the way forward [Internet]. Digitus Biometrics (2020). Cited 10 July 2020. [Online]. https://digitus-biometrics.com/blog/4-reasons-why-biometric-security-is-the-way-forward/
M. Ghadawala, 7 key benefits of security with biometrics. GlobalSign GMO Internet, Inc. (2020). [Online]. https://www.globalsign.com/en/blog/7-benefits-security-with-biometrics. Accessed 10/07/2020
A. Chan, T. Halevi, N. Memon, Leap motion controller for authentication via hand geometry and gestures, in Lecture Notes in Computer Science (2015), pp. 13–22
J. Tian, W. Xu, Y. Cao, S. Wang, Challenge-response authentication using in-air handwriting style verification. IEEE Trans. Depend. Secur. Comput. 17(1), 51–64 (2020)
G. Xiao, M. Milanova, M. Xie, Secure behavioral biometric authentication with leap motion, in 4th International Symposium on Digital Forensic and Security (ISDFS) (2016)
S. Kamaishi, R. Uda, Biometric authentication by handwriting with single direction using self-organizing maps, in 11th International Conference on Ubiquitous Information Management and Communication (2017)
S. Imura, H. Hosobe, A hand gesture-based method for biometric authentication, in Human-Computer Interaction Theories, Methods, and Human Issues (2018), pp. 554–566
K. Maruyama, J. Shin, C. Kim, C. Chen, User authentication using leap motion, in International Conference on Research in Adaptive and Convergent Systems (2017)
T. Manabe, H. Yamana, Two-factor authentication using leap motion and numeric keypad, in HCI for Cybersecurity, Privacy and Trust (2019), pp. 38–51
M. Atas, Hand tremor based biometric recognition using leap motion device. IEEE Access 5, 23320–23326 (2017)
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Hassan, M.A., Shadman, Q., Chowdhury, M.H., Al Hasan, S., Uddin, J. (2021). User Authentication Using Password and Hand Gesture with Leap Motion Sensor. In: Balas, V.E., Hassanien, A.E., Chakrabarti, S., Mandal, L. (eds) Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing. Lecture Notes on Data Engineering and Communications Technologies, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-33-4968-1_2
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DOI: https://doi.org/10.1007/978-981-33-4968-1_2
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