End-to-End Model-Based Gait Recognition
End-to-End Model-Based Gait Recognition
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DOI：
10.1007/978-3-030-69535-4_1
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发表时间：
2020
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影响因子：
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通讯作者：
Xiang Li-;Yasushi Makihara;Chi Xu;Y. Yagi;Shiqi Yu;Mingwu Ren
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文献类型：
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作者：
Xiang Li-;Yasushi Makihara;Chi Xu;Y. Yagi;Shiqi Yu;Mingwu Ren
大多数现有的步态识别方法采用两步过程：预处理步骤提取轮廓或骨架，然后进行识别。在本文中，我们提出了一种端到端的基于模型的步态识别方法。具体来说，我们采用皮肤多人线性（SMPL）模型的人体建模，并估计其参数使用预先训练的人体网格恢复（HMR）网络。由于预训练的HMR不是面向步态的，我们在端到端步态识别框架中对其进行微调。为了科普步态数据集和用于预训练HMR的数据集之间的差异，我们在步态数据集中的轮廓掩模和由可微渲染器产生的估计SMPL模型的渲染轮廓之间引入重建损失。这使我们能够使用地面真实关节位置在没有监督的情况下使HMR适应步态数据集。OU-MVLP和CASIA-B数据集的实验结果表明，所提出的方法在步态识别和验证场景中具有最先进的性能，这是所提出的端到端基于模型的框架产生的明确解开的姿势和形状特征的直接结果。
Most existing gait recognition approaches adopt a two-step procedure: a preprocessing step to extract silhouettes or skeletons followed by recognition. In this paper, we propose an end-to-end model-based gait recognition method. Specifically, we employ a skinned multi-person linear (SMPL) model for human modeling, and estimate its parameters using a pre-trained human mesh recovery (HMR) network. As the pre-trained HMR is not recognition-oriented, we fine-tune it in an end-to-end gait recognition framework. To cope with differences between gait datasets and those used for pre-training the HMR, we introduce a reconstruction loss between the silhouette masks in the gait datasets and the rendered silhouettes from the estimated SMPL model produced by a differentiable renderer. This enables us to adapt the HMR to the gait dataset without supervision using the ground-truth joint locations. Experimental results with the OU-MVLP and CASIA-B datasets demonstrate the state-of-the-art performance of the proposed method for both gait identification and verification scenarios, a direct consequence of the explicitly disentangled pose and shape features produced by the proposed end-to-end model-based framework.