Title: Knowledge-based gait behavioural authentication through a machine learning approach

Authors: Gogineni Krishna Chaitanya; Krovi Raja Sekhar

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522502 Andhra Pradesh, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522502 Andhra Pradesh, India

Abstract: Smartphone-based biometrics assumes a significant job in advanced mobile phones to protect the personal data, which are kept in the smart devices. The verification pattern such as pattern and password is broadly utilised in the mobile devices. But, these verification ways may not be more secured. Thus, the gait based pattern verification process is presented to confirm the genuine user. A novel kernel compactness African buffalo adaptive boosting (KCAB-AdaBoost) model is proposed to distinguish the authenticated user. To examine this current work model, the unauthenticated user's movement is also trained and tests simultaneously with the authenticated user's movement. Moreover, if the movement is coordinated, then the mobile device will be unlocked consequently or else it cannot be unlocked. Lastly, the adequacy of the proposed model is established by achieving better exactness and less error rate.

Keywords: biometrics; authentication; gait recognition; kernel compactness approximation; adaptive boosting; optimisation.

DOI: 10.1504/IJBET.2021.115998

International Journal of Biomedical Engineering and Technology, 2021 Vol.36 No.1, pp.25 - 42

Received: 29 May 2020
Accepted: 23 Aug 2020
Published online: 06 Jul 2021 *

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