Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames, and their recognition performance will sharply decrease if the frame number drops. In the real-world scenario, it is impossible to always obtain a sufficient number of gait frames for each subject due to many reasons, e.g., occlusion and illumination. Therefore, it is necessary to improve the gait recognition performance when the available gait frames are limited. This paper starts with three different strategies, aiming at producing more input frames and eliminating the generalization error cause by insufficient input data. Meanwhile, a two-branch network is also proposed in this paper to formulate robust gait representations from the original and new generated input gait frames. According to our experiments, under the limited gait frames being used, it was verified that the proposed method can achieve a reliable performance for gait recognition.

中文翻译：

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使用一些步态帧进行步态识别


步态被认为是基于视频的监控应用中的替代生物识别技术，因为它可以用来识别远距离的个人，而无需他们的互动和合作。近年来，人们提出了许多步态识别方法，旨在减少外部因素造成的影响。然而，这些方法大多数都是基于足够的输入步态帧而开发的，如果帧数下降，它们的识别性能将急剧下降。在现实场景中，由于遮挡和照明等多种原因，不可能始终为每个主体获得足够数量的步态帧。因此，当可用步态帧有限时，有必要提高步态识别性能。本文从三种不同的策略开始，旨在产生更多的输入帧并消除由于输入数据不足而导致的泛化误差。同时，本文还提出了一种两分支网络，用于根据原始和新生成的输入步态帧制定鲁棒的步态表示。根据我们的实验，在使用的有限步态框架下，验证了所提出的方法可以实现可靠的步态识别性能。