通过深度残差网络和多分支特征融合进行步态识别
Gait Recognition via Deep Residual Networks and Multi-Branch Feature Fusion
AI总结：
本文提出一种高精度步态识别框架，通过多分支架构结合深度残差学习，融合步态动态与身体形状特征，提升在视角变化、着装变化等干扰下的识别性能。
摘要：
步态识别已成为安防和安全应用中具有吸引力的生物特征模态，具有非侵入性、抗伪装和远距离识别等固有优势。然而，现有方法难以全面捕捉和利用人体运动中丰富的生物特征线索，特别是在存在协变量干扰（如视角变化、着装变化和携带条件）的情况下。本文提出一种高精度步态识别框架，通过基于深度残差学习的多分支架构深度提取并协同融合步态动态与身体形状特征。具体来说，首先使用高分辨率网络（HRNet）进行稳健的骨骼关键点估计，在低分辨率输入下保留细粒度的空间信息。然后从提取的姿态序列中构建三个互补的特征分支——身体比例、步态速度和骨骼运动。在深度特征提取模块中，利用50层残差网络（ResNet-50）来捕捉层次丰富且具有判别性的表示。为了有效整合异构特征流，我们设计了受通道注意力机制启发的多分支特征融合（MFF）模块，通过学习的激活参数动态分配各分支的贡献权重。在跨视角多条件CASIA-B基准上的大量实验表明，我们的方法在正常行走条件下实现了94.52%的排名1准确率，是基于骨骼的方法中在穿外套条件下表现最好的识别性能。
英文摘要：
Gait recognition has emerged as a compelling biometric modality for surveillance and security applications, offering inherent advantages such as non-intrusiveness, resistance to disguise, and long-range identification capability. However, prevailing approaches struggle to comprehensively capture and exploit the rich biometric cues embedded in human locomotion, particularly under covariate interference including viewpoint variation, clothing change, and carrying conditions. In this paper, we present a high-precision gait recognition framework that deeply extracts and synergistically fuses gait dynamics with body shape characteristics through a multi-branch architecture grounded in deep residual learning. Specifically, we first employ the High-Resolution Network (HRNet) to perform robust skeletal keypoint estimation, preserving fine-grained spatial information even under low-resolution inputs. We then construct three complementary feature branches -- body proportion, gait velocity, and skeletal motion -- from the extracted pose sequences. A 50-layer Residual Network (ResNet-50) backbone is leveraged within a deep feature extraction module to capture hierarchically rich and discriminative representations. To effectively integrate heterogeneous feature streams, we design a Multi-Branch Feature Fusion (MFF) module inspired by channel-wise attention mechanisms, which dynamically allocates contribution weights across branches through learned activation parameters. Extensive experiments on the cross-view multi-condition CASIA-B benchmark demonstrate that our method achieves a Rank-1 accuracy of 94.52\% under normal walking, with the best recognition performance among skeleton-based methods for the coat-wearing condition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
