Cross-view gait recognition using view-dependent discriminative analysis
Cross-view gait recognition using view-dependent discriminative analysis
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DOI：
10.1109/btas.2014.6996272
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Al Mansur;Yasushi Makihara;D. Muramatsu;Y. Yagi
中科院分区：
文献类型：
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作者：
Al Mansur;Yasushi Makihara;D. Muramatsu;Y. Yagi
步态是一种独特的和有前途的行为生物识别技术，它允许在距离摄像头一定距离的情况下对人进行身份验证。由于在真实的世界中，由于摄像机位置/姿态和行走方向的差异，通常从不同的视图中提取匹配的步态特征对，因此科普跨视图步态识别是重要的。在本文中，我们提出了一种判别方法，跨视图步态识别使用视图相关的投影矩阵，不像现有的判别方法，只利用一个共同的投影矩阵不同的意见。我们通过使用两个公开的步态数据集进行跨视图步态识别实验，证明了所提出方法的有效性。此外，由于判别分析的成功依赖于训练样本量，我们展示了两个步态数据集之间的迁移学习效果，并提供了所提出的方法对训练受试者数量从10到大约1,000个受试者的严格灵敏度分析。
Gait is a unique and promising behavioral biometrics which allows to authenticate a person even at a distance from the camera. Since a matching pair of gait features are often drawn from different views due to differences in camera position/attitude and walking directions in the real world, it is important to cope with cross-view gait recognition. In this paper, we propose a discriminative approach to cross-view gait recognition using view-dependent projection matrices, unlike the existing discriminant approaches which utilize only a single common projection matrix for different views. We demonstrated the effectiveness of the proposed method through cross-view gait recognition experiments with two publicly available gait datasets. In addition, since the success of the discriminant analysis relies on the training sample size, we show the effect of transfer learning across two gait datasets as well as provide the rigorous sensitivity analysis of the proposed method against the number of training subjects ranging from 10 to approximately 1,000 subjects.