Biometric Identification using Gait Analysis: Methods, Characteristics, and Difficulties

Gait recognition is a rapidly developing field of study that applies biometrics to identify people by their distinctive walking patterns. Gait recognition primarily performs identification authentication by utilizing the unique postures of each individual. These methods aim to recognize people according to their intrinsic, visible qualities, regardless of their clothing or personal effects. While the topic suggests a long-standing challenge, the majority of solutions developed to tackle it suffer from a number of drawbacks, such as poor feature extraction and low classification rates. Deep learning-based methods, on the other hand, have lately become a powerful toolkit to tackle almost every picture and computer vision-related issue, and they also yield excellent gait identification results. With an emphasis on deep learning methodologies, we hope to highlight their advantages and expose their drawbacks to help researchers make well-informed selections about the many variables to be considered for learning gait identification. Additionally, various case studies on implication of gait recognition using different deep learning models are discussed to provide an overview of the practical applications of this technique.