Gait-based features provide the potential for a subject to be recognized even from a low-resolution image sequence, and they can be captured at a distance without the subject’s cooperation. Person recognition using gait-based features (gait recognition) is a promising real-life application. However, several body parts of the subjects are often occluded because of beams, pillars, cars and trees, or another walking person. Therefore, gait-based features are not applicable to approaches that require an unoccluded gait image sequence. Occlusion handling is a challenging but important issue for gait recognition. In this paper, we propose silhouette sequence reconstruction from an occluded sequence (sVideo) based on a conditional deep generative adversarial network (GAN). From the reconstructed sequence, we estimate the gait cycle and extract the gait features from a one gait cycle image sequence. To regularize the training of the proposed generative network, we use adversarial loss based on triplet hinge loss incorporating Wasserstein GAN (WGAN-hinge). To the best of our knowledge, WGAN-hinge is the first adversarial loss that supervises the generator network during training by incorporating pairwise similarity ranking information. The proposed approach was evaluated on multiple challenging occlusion patterns. The experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art benchmarks.

中文翻译：

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时空轮廓序列重构，用于步态识别

基于步态的特征甚至可以从低分辨率图像序列中识别出被摄体，并且无需与被摄体合作就可以在远处捕获它们。使用基于步态的功能进行人识别（步态识别）是一种有前途的现实应用。但是，由于横梁，柱子，汽车，树木或其他行走的人，经常会遮挡受试者的多个身体部位。因此，基于步态的特征不适用于要求步态图像序列无遮挡的方法。遮挡处理是步态识别中一个具有挑战性但重要的问题。在本文中，我们提出了基于条件深度生成对抗网络（GAN）的遮挡序列（sVideo）的轮廓序列重构。从重建的序列 我们估计步态周期并从一个步态周期图像序列中提取步态特征。为了规范提出的生成网络的训练，我们使用基于Wasserstein GAN（WGAN-hinge）的三重铰链损失的对抗性损失。据我们所知，WGAN铰链是第一个通过合并成对相似性排名信息来监督训练期间发电机网络的对抗性损失。在多种具有挑战性的遮挡模式下对提出的方法进行了评估。实验结果表明，所提出的方法优于现有的最新基准。据我们所知，WGAN铰链是第一个通过合并成对相似性排名信息来监督训练期间发电机网络的对抗性损失。在多种具有挑战性的遮挡模式下对提出的方法进行了评估。实验结果表明，所提出的方法优于现有的最新基准。据我们所知，WGAN铰链是第一个通过合并成对相似性排名信息来监督训练期间发电机网络的对抗性损失。在多种具有挑战性的遮挡模式下对提出的方法进行了评估。实验结果表明，所提出的方法优于现有的最新基准。