HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition

• URL: http://arxiv.org/abs/2204.10238v1
• Date: Thu, 21 Apr 2022 16:13:58 GMT
• Title: HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition
• Authors: Md. Bakhtiar Hasan, Tasnim Ahmed, Md. Hasanul Kabir
• Abstract summary: HEATGait is a gait recognition system that improves the existing multi-scale convolution graph by efficient hop-extraction technique to alleviate the issue. We propose a powerful feature extractor that utilizes ResG to achieve state-of-the-art performance in model-based gait recognition on the CASIA-BCN gait dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Biometric authentication using gait has become a promising field due to its unobtrusive nature. Recent approaches in model-based gait recognition techniques utilize spatio-temporal graphs for the elegant extraction of gait features. However, existing methods often rely on multi-scale operators for extracting long-range relationships among joints resulting in biased weighting. In this paper, we present HEATGait, a gait recognition system that improves the existing multi-scale graph convolution by efficient hop-extraction technique to alleviate the issue. Combined with preprocessing and augmentation techniques, we propose a powerful feature extractor that utilizes ResGCN to achieve state-of-the-art performance in model-based gait recognition on the CASIA-B gait dataset.

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