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Vision-based approaches towards person identification using gait

Visual surveillance has exponentially increased the growth of security devices and systems in the digital era. Gait-based person identification is an emerging biometric modality for automatic visual surveillance and monitoring as the walking patterns highly correlate to the subject’s identity. The scientific research on person identification using gait has grown dramatically over the past two decades due to its several benefits. It does not require active collaboration from users and can be performed without their cooperation. It is difficult to be impersonated and identification can be validated from low-resolution videos and with simple instrumentation. This paper presents a comprehensive overview of the exiting techniques, their key stages, and recent developments in vision-based person identification using gait. We reviewed the historical research on gait locomotion and explain that how it is used to recognize the identity. The article summarizes the different types of features that have been proposed to encode the biomechanics of gait and also groups them into different categories and subcategories based upon the similarity in their implementation. We also present the impact of different covariate factors that affect the performance of gait recognition systems and also discuss the recent works to cope with these challenges. Furthermore, a comparison of the recognition accuracies reported by the existing algorithms to assess their performance under verification and identification mode is also presented. A detailed summary of publicly available vision-based gait databases is also provided. Finally, it offers insight into the challenges and open problems for future perspectives in the field of gait recognition that can help to set the directions for future research in this field.


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