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Human gait recognition: A systematic review
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AI
DOI：10.1007/s11042-023-15079-5.png)
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Abstract
En 中文
A biometric system is a technology that utilizes an individual's unique physiological and behavioral characteristics to identify and authenticate them. It falls under the category of pattern recognition. Gait recognition, specifically the identification of individuals based on their walking patterns, has garnered significant attention from researchers due to its potential to accurately identify individuals from a distance. Gait recognition systems involve a complex integration of technical, operational and definitional choices and have been applied in a variety of contexts such as security, medical examinations, identity management, and access control. The utilization of gait recognition methods and tools has led to the development of various useful and widely accepted applications. This article provides an overview of the various techniques and approaches employed in gait recognition, including the framework, history, and parameters utilized. The article also delves into the different classifiers, both traditional and deep learning-based, used in the field. Additionally, it examines the different types of datasets utilized in experimental research and the methodology for evaluating articles on gait recognition. With its potential in security applications, gait recognition is expected to have a wide range of future applications.
Keywords:
Gait
Model-free
Model-based
Covariates
Sensors
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W
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