GaitAE: A Cognitive Model-Based Autoencoding Technique for Gait Recognition

Rui Li, Huakang Li (), Yidan Qiu, Jinchang Ren, Wing W. Y. Ng and Huimin Zhao ()
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Rui Li: College of Fine Arts, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Huakang Li: Pattern Recognition and Intelligent System Laboratory, School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Yidan Qiu: Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for the Study of Applied Psychology, School of Psychology, South China Normal University, Guangzhou 510631, China
Jinchang Ren: National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK
Wing W. Y. Ng: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
Huimin Zhao: Pattern Recognition and Intelligent System Laboratory, School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China

Mathematics, 2024, vol. 12, issue 17, 1-11

Abstract: Gait recognition is a long-distance biometric technique with significant potential for applications in crime prevention, forensic identification, and criminal investigations. Existing gait recognition methods typically introduce specific feature refinement modules on designated models, leading to increased parameter volume and computational complexity while lacking flexibility. In response to this challenge, we propose a novel framework called GaitAE. GaitAE efficiently learns gait representations from large datasets and reconstructs gait sequences through an autoencoder mechanism, thereby enhancing recognition accuracy and robustness. In addition, we introduce a horizontal occlusion restriction (HOR) strategy, which introduces horizontal blocks to the original input sequences at random positions during training to minimize the impact of confounding factors on recognition performance. The experimental results demonstrate that our method achieves high accuracy and is effective when applied to existing gait recognition techniques.

Keywords: gait recognition; biologic recognition; autoencoder; deep learning; computer vision; covariate reduction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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