Abstract
Automated authentication systems are currently the research trend, as security is given utmost importance. Biometric systems are considered reliable; however, they demand human intervention or cooperation for data collection. On the contrary, gait represents the walking pattern of an individual, which is unique, and it does not require human intervention. However, gait authentication systems are confronted by numerous challenges such as illumination, different angles and poor lighting condition. This article presents a gait authentication scheme, which is based on grey wolf optimization algorithm-optimized Gabor features. The potential features are then selected by information gain ratio, and the kernelized extreme learning machine is employed for authentication. The proposed scheme is analysed with respect to recognition accuracy, precision, recall, F1-score and time consumption against existing approaches, where the results show that the proposed scheme performs better.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ms. Ambika K. The first draft of the manuscript was written by Ms. Ambika K., and the supervisor has assisted on the preparation of the manuscript. All authors read and approved the final manuscript.
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Ambika, K., Radhika, K.R. Model-free supervised learning-based gait authentication scheme based on optimized gabor features. Soft Comput 27, 5053–5062 (2023). https://doi.org/10.1007/s00500-023-08029-8
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DOI: https://doi.org/10.1007/s00500-023-08029-8