论文标题
热程：基于图形卷积的步态识别中的跃接的邻接技术
HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition
论文作者
论文摘要
由于其不引人注目的性质，使用步态的生物识别验证已成为一个有希望的领域。基于模型的步态识别技术的最新方法利用时空图来优雅地提取步态特征。但是，现有方法通常依靠多尺度运算符来提取关节之间的长距离关系，从而导致加权。在本文中，我们提出了Heatgait，这是一种步态识别系统，通过有效的啤酒花萃取技术来改善现有的多尺度图卷积以减轻问题。结合预处理和增强技术，我们提出了一个强大的功能提取器，该功能提取器利用RESGCN在CASIA-B GAIT数据集中的基于模型的步态识别中实现最先进的性能。
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.
