Fusion of static and dynamic body biometrics for gait recognition
Fusion of static and dynamic body biometrics for gait recognition
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DOI：
10.1109/iccv.2003.1238660
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发表时间：
2003-10
影响因子：
8.4
通讯作者：
Liang Wang;Huazhong Ning;T. Tan;Weiming Hu
中科院分区：
文献类型：
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作者：
Liang Wang;Huazhong Ning;T. Tan;Weiming Hu
基于视觉的远距离人体识别最近引起了计算机视觉研究人员越来越多的兴趣。本文提出了一种结合静态和动态人体特征的人体识别算法。对于涉及步行者的每个序列，将分割的运动轮廓的时间姿态变化表示为相关联的复向量配置序列，然后使用Procrustes形状分析方法进行分析，以获得紧凑的外观表示，称为身体的静态信息。此外，一个基于模型的方法，提出了一个冷凝框架下跟踪的步行者，并进一步恢复下肢关节角轨迹，称为步态的动态信息。从行走视频获得的静态和动态线索可以独立地用于使用最近的样本分类器的识别。它们在决策层上使用不同的规则组合进行融合，以提高识别和验证的性能。对20个主题数据集的实验结果表明了该算法的可行性。
Vision-based human identification at a distance has recently gained growing interest from computer vision researchers. This paper describes a human recognition algorithm by combining static and dynamic body biometrics. For each sequence involving a walker, temporal pose changes of the segmented moving silhouettes are represented as an associated sequence of complex vector configurations and are then analyzed using the Procrustes shape analysis method to obtain a compact appearance representation, called static information of body. In addition, a model-based approach is presented under a Condensation framework to track the walker and to further recover joint-angle trajectories of lower limbs, called dynamic information of gait. Both static and dynamic cues obtained from walking video may be independently used for recognition using the nearest exemplar classifier. They are fused on the decision level using different combinations of rules to improve the performance of both identification and verification. Experimental results of a dataset including 20 subjects demonstrate the feasibility of the proposed algorithm.