Data preprocessing and feature selection techniques in gait recognition: A comparative study of machine learning and deep learning approaches

The study of gait recognition, a biometric application that identifies individuals based on their unique walking patterns, is an evolving field. In this paper, we conduct a literature review to compare the per-formance of machine learning and deep learning approaches in covariate conditions, focusing on the spe-cific aspects of deep learning pipelines in gait recognition. We highlight commonly used strategies and open problems in identification based on behavioral traits and propose future perspectives for researchers in this field. Through our investigation, we aim to provide insights that will aid researchers in develop-ing informed decisions when to take which data preprocessing technique in designing gait recognition systems. Our paper provides a comprehensive exposition of machine learning versus deep learning archi-tectures and pipelines for biometric applications using human gait and serves as a valuable resource for researchers in this area. & COPY; 2023 Published by Elsevier B.V.