Verification of pattern unlock and gait behavioural authentication through a machine learning approach

Abstract

Purpose

The existing authentication procedures (pin, pattern, password) are not very secure. Therefore, the Gait pattern authentication scheme is introduced to verify the own user. The current research proposes a running Gaussian grey wolf boosting (RGGWB) model to recognize the owner.

Design/methodology/approach

The biometrics system plays an important role in smartphones in securing confidential data stored in them. Moreover, the authentication schemes such as passwords and patterns are widely used in smartphones.

Findings

To validate this research model, the unauthenticated user's Gait was trained and tested simultaneously with owner gaits. Furthermore, if the gait matches, the smartphone unlocks automatically; otherwise, it rejects it.

Originality/value

Finally, the effectiveness of the proposed model is proved by attaining better accuracy and less error rate.

Citation

Chaitanya, G.K. and Raja Sekhar, K. (2022), "Verification of pattern unlock and gait behavioural authentication through a machine learning approach", International Journal of Intelligent Unmanned Systems, Vol. 10 No. 1, pp. 48-64. https://doi.org/10.1108/IJIUS-09-2020-0048