Gait is a biometry characterized by the identification of individuals by the way they walk. It is recently gaining evidence because it can be collected at distance and does not require subject cooperation, which is desirable on surveillance scenarios. Despite these advantages, the literature reports challenging situations where gait recognition is not accurate and although exist works that try to address these problems, most of them uses silhoettes, which carry appearance information that confounds with gait. Because of this limitation, a pose estimation method that use information of frames is employed for gait recognition and a multilayer perception, called PoseFrame, is created. As the focus of gait is the classification of a whole walking sequence, the results based on the frames are temporally aggregated for final classification. The method is tested on CASIA Dataset A, having accuracy above other pose-based works; and on CASIA Dataset B, achieving the best results in some situations. An ablation study is also performed, finding that the arms and feet are the most important body parts for gait recognition.

步态是一种生物特征，其特征是通过个体的行走方式来识别个体。最近它得到了证据，因为它可以在远处收集并且不需要主题合作，这在监视场景中是理想的。尽管有这些优点，文献报道了挑战性的情况，其中步态识别不准确，尽管存在尝试解决这些问题的作品，但大多数都使用silhoettes，其携带与步态相混淆的外观信息。由于此限制，将使用帧信息的姿势估计方法用于步态识别，并创建了称为PoseFrame的多层感知。由于步态的重点是整个步行序列的分类，因此将基于帧的结果暂时汇总起来以进行最终分类。该方法在CASIA数据集A上进行了测试，其准确性高于其他基于姿势的工作；在CASIA数据集B上，在某些情况下可达到最佳结果。还进行了消融研究，发现手臂和脚是步态识别最重要的身体部位。