【作者】 陈彦；

【导师】 梁继民；

【作者基本信息】 西安电子科技大学 ， 电路与系统， 2007， 硕士

【摘要】 步态识别技术是依据人行走方式的不同对人的身份进行识别的一种生物识别技术。与其它生物特征(人脸、虹膜、指纹等)相比,步态识别技术是唯一一个可以进行远距离身份识别的生物识别技术。此外,步态识别还具有非接触、不唐突、难以伪装等优点。随着银行、机场等安全敏感场合对自动身份识别系统的迫切要求,步态识别技术引起人们越来越广泛的重视。独立分量分析(Independent Component Analysis, ICA)是继主分量分析(Principal Component Analysis, PCA)之后的又一分析多维数据的有力工具。ICA在人脸识别方面显示出了它良好的分类性能,而在步态识别方面,ICA的应用却很少。本文在独立分量分析的基础上,提出了两种新的步态识别方法——基于ICA结构I的步态识别方法和基于ICA结构II的步态识别方法。本文试图去探寻影响步态识别性能的因素及其影响程度。基于ICA结构I的步态识别方法首先对图像序列进行PCA运算,得到相互正交的PC轴;然后对这些PC轴进行ICA运算,得到相互统计独立的IC轴;把步态序列投影到IC轴上,就得到了相应的投影系数;为了降低选取数据的随机性,对同一个样本不同周期序列得到的各组系数求平均从而得到平均系数,样本的步态特征就由这些平均系数来表征;最后,对平均系数以马氏距离为标准用最近邻法做识别。该方法用包含71个样本的USF步态数据库进行评估,实验证明,该方法具有较好的识别性能。基于ICA结构II的步态识别方法首先对训练样本进行PCA运算,得到PC特征空间;然后把样本图像序列投影到这个PC特征空间上,得到不相关的PC系数;再对不相关的PC系数进行ICA运算得到相互统计独立的IC系数;和结构I类似,对同一个样本不同周期序列得到的各组IC系数求平均从而得到平均IC系数,样本的步态特征就由平均IC系数来表征;最后以测试样本和各训练样本的平均IC系数间的马氏距离为标准用最近邻法作识别。该方法分别用包含25个样本的CMU MoBo步态数据库和包含71个样本USF步态数据库对其进行评估,实验证明,该方法具有较好的识别性能和较高的计算效率。更多还原

【Abstract】 Gait recognition tries to identify a person by the manner he walks. Compared with other kinds of biometrics such as face, iris, and fingerprint, gait has the merits of non-contact, unobtrusive, hard to disguise, and can be used for human recognition at a distance when other biometrics are obscured. It inspires the biometric recognition researchers working on it because of the increasing demand for automated human identification systems in security sensitive occasions.After principal component analysis (PCA), independent component analysis (ICA) is another useful tool for multidimensional data analyzing. ICA has shown its good classification performance in face recognition, but there’s little ICA application to gait recognition. In this thesis, two new methods for gait recognition based on ICA are proposed. This thesis tries to find what factors affect gait recognition and to what extent.The first method is based on ICA Architecture I. Firstly, PCA is performed on image sequences of all persons and the PC axes are obtained. Then, ICA is performed on these PC axes to get the statistically independent IC axes. After that, the image sequences are projected onto these independent IC axes and the coefficients are obtained. The coefficients from the same person are averaged and the mean coefficients are used to be the representation of individual gait characteristics. For improving computational efficiency, a fast and robust method named InfoMax algorithm is used for calculating independent components. Gait recognition performance of the proposed method was evaluated by using USF gait dataset. Experiment results show the efficiency and advantages of the first method.The second method is based on ICA Architecture II. Firstly, PCA is performed on image sequences of all persons to get the uncorrelated PC coefficients. Then, ICA is performed on the PC coefficients to obtain the independent IC coefficients. The IC coefficients from the same person are averaged and the mean IC coefficients are used to represent individual gait characteristics. InfoMax algorithm is used for calculating independent components as in the first method. Gait recognition performance of the second method is evaluated by using CMU MoBo dataset and USF Challenge gait dataset. Experiment results show the efficiency and advantages of the second method.更多还原