Towards Enhanced Biometric Systems: Insights into Human Gait Recognition

Gait recognition is a biometric method of identifying people based on their walking pattern, which is the result of the coordinated action of the brain, nervous system, and muscles. Historically, gait analysis has been performed using a simple visual approach, but now several new tools have emerged, such as wearable sensors and vision-based motion capture, and a nonintrusive approach to facilitate gait analysis. In this paper, a detailed review of human gait recognition is presented. This is followed by a bibliometric analysis of the trends of publication, major sources, and country-wise research contributions. The current state of the art of research is then summarized, ranging from handcrafted feature-based approaches to recent deep learning architectures and the public datasets and benchmarks that have been used, from ۲۰۰۵ onwards, to demonstrate how performance has improved over time using the CASIA-B dataset. In addition to the technical aspects, the applications of gait recognition in surveillance, healthcare, sports, and defense are discussed. Last, we discuss some future directions, including cross-domain learning, transformer-based models, etc., and present key challenges, including domain shift, covariate sensitivity, data dependency, and privacy concerns. The collective discussions provide a framework for researchers and practitioners in gait recognition and identify the gaps that still need to be filled to enable more widespread adoption of gait recognition in practice.

کلیدواژه ها:

Gait Recognition ، Deep learning ، soft biometric ، gait cycle ، biometric system