论文标题
密集视图GEIS集：基于密集视图gan的步态识别的视图空间覆盖
Dense-View GEIs Set: View Space Covering for Gait Recognition based on Dense-View GAN
论文作者
论文摘要
步态识别已被证明对长距离人类认可有效。但是，观察步态特征的差异将大大改变人类外观并降低其性能。大多数现有的步态数据集通常以十二个不同角度甚至更多的角度收集数据。有限的视图角度将阻止学习更好的视图不变功能。如果我们以1度间隔收集各个角度的数据，它可以进一步提高步态识别的鲁棒性。但是收集这种数据集是耗时的和劳动消耗的。因此，在本文中，我们引入了一个密集的GEIS集（DV-GEIS）来应对有限视角的挑战。该集合可以覆盖整个视图空间，距离为0度到180度的视角，间隔为1度。此外，提出了密集视图gan（DV-GAN）来合成此密集的视图集。 DV-GAN由发电机，歧视器和监视器组成，其中监视器旨在保存人类的识别和查看信息。在CASIA-B和OU-ISIR数据集上评估了所提出的方法。实验结果表明，由DV-GAN合成的DV-GEIS是学习更好视图不变特征的有效方法。我们认为，浓缩视图产生的样本的想法将进一步改善步态识别的发展。
Gait recognition has proven to be effective for long-distance human recognition. But view variance of gait features would change human appearance greatly and reduce its performance. Most existing gait datasets usually collect data with a dozen different angles, or even more few. Limited view angles would prevent learning better view invariant feature. It can further improve robustness of gait recognition if we collect data with various angles at 1 degree interval. But it is time consuming and labor consuming to collect this kind of dataset. In this paper, we, therefore, introduce a Dense-View GEIs Set (DV-GEIs) to deal with the challenge of limited view angles. This set can cover the whole view space, view angle from 0 degree to 180 degree with 1 degree interval. In addition, Dense-View GAN (DV-GAN) is proposed to synthesize this dense view set. DV-GAN consists of Generator, Discriminator and Monitor, where Monitor is designed to preserve human identification and view information. The proposed method is evaluated on the CASIA-B and OU-ISIR dataset. The experimental results show that DV-GEIs synthesized by DV-GAN is an effective way to learn better view invariant feature. We believe the idea of dense view generated samples will further improve the development of gait recognition.
