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| Abstract: |
| Gait recognition, a promising biometric technology, relies on analyzing individuals walking patterns and offers a non-intrusive and convenient approach to identity verification. However, gait recognition accuracy is often compromised by external factors such as changes in viewpoint and attire, which present substantial challenges in practical applications. To enhance gait recognition performance under diverse viewpoints and complex conditions, a global-local part-shift network is proposed in this paper. This framework integrates two novel modules: the part-shift feature extractor and the dynamic feature aggregator. The part-shift feature extractor strategically shifts body parts to capture the intrinsic relationships between non-adjacent regions, enriching the recognition process with both global and local spatial features. The dynamic feature aggregator addresses long-range dependency issues by incorporating multi-range temporal modeling, effectively aggregating information across parts and time steps to achieve a more robust recognition outcome. Comprehensive experiments on the CASIA-B dataset demonstrate that the proposed global-local part-shift network delivers superior performance compared with state-of-the-art methods, highlighting its potential for practical deployment. |
| Key words: gait recognition global-local feature part-shift |
| DOI：10.11916/j.issn.1005-9113.24064 |
| Clc Number:TP394.41 |
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| Descriptions in Chinese: |
| 一种基于全局-局部部位顺序重排的步态识别网络 李桂芝, 方炜炜 北京信息科技大学 计算机学院，北京 100192 摘要：步态识别是一种具有广阔前景的生物特征识别技术，通过分析个体独特的行走模式实现身份验证，具备非侵入性与便利性等优势。然而，实际应用中，由于视角变化和服饰差异等外部因素的干扰，步态识别的准确性往往受到影响，面临巨大挑战。为提升在多视角及复杂条件下步态识别的性能，本文提出一种全局-局部部位迁移网络（Global-Local Part-Shift Network）。该框架包含两个创新模块：部位迁移特征提取器（Part-Shift Feature Extractor）和动态特征聚合器（Dynamic Feature Aggregator）。其中，部位迁移特征提取器通过策略性地迁移身体各个部位，捕获非相邻区域之间的内在关系，以丰富全局与局部空间特征；动态特征聚合器则通过多尺度时间建模来解决长距离依赖问题，有效聚合不同部位和时间步之间的信息，从而实现更稳健的识别效果。在CASIA-B公开数据集上的实验结果表明，本文提出的全局-局部部位迁移网络在性能上优于当前主流先进方法，展现了其良好的实际应用潜力。 关键词：步态识别；全局-局部特征；部位顺序重排 |
