Improving robustness of gait recognition based on deep learning

Gait recognition is a technology that identifies human ID according to the human unique biometric gait feature. It has two popular categories. One category of gait recognition methods is appearance-based algorithms, which usually extracts human silhouettes as the initial input feature and achieves high recognition rates. However, the silhouette-based feature is easily affected by the view, clothing, bag, and other external variations. Another category is based on model-based algorithms, one popular model-based feature is extracted from human skeletons. The skeleton-based feature is robust to many variations because it is less sensitive to human shape. However, the performance of skeleton-based methods suffers from recognition accuracy loss due to limited input information. This thesis proposes multiple deep learning-based frameworks to address multiple issues for the appearance-based methods and model-based methods. In each case, the proposed methods achieve high results with significant improvement to the current technologies.

Name thesis:degree_name

Ph.D. (Doctor of Philosophy)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Networking and Communication Systems (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2024

Author dc:creator

• Liao, Rijun

Advisors dc:contributor.advisor

• Li, Zhu
• Song, Sejun

Handle dc:identifier.uri

https://hdl.handle.net/10355/100721

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/100721