中文摘要：

为弥补单一模型识别能力的不足,削弱因步速、衣着、光照等变化的影响,提出小波分解（WD）、不变矩（IM）并结合骨架理论（ST）提取步态特征参数的新方法。其技术流程为：先分割出人体目标轮廓,并将其规格化叠加处理,获取步态特征图;再将小波分解与不变矩结合,提取含人体整体模型信息的矩参数;同时将步态特征图骨架化,提取含人体简化模型信息的骨架特征参数;最后将矩参数与骨架参数作为识别参量,输入支持向量机（SVM）进行步态识别。使用自建的天津大学红外步态数据库（TIGD）进行试验,其正确识别率为84%～92%。表明多参数模型相结合的方法有利于提取步态的本质结构特征。

英文摘要：

In order to improve the recognition capability with single model and restrain the impact of noise （walking speed, clothing, illumination, etc） in gait recognition, a novel technique of feature extraction was presented for gait parameters in this paper. This method was based on wavelet decomposition（WD）, invariant moments （IM） and skeleton theory（ST）. Body silhouette sequences were extracted and normalized. The sequences were added together and the gait feature image could be achieved. The moment parameters with information of integral model were obtained by using wavelet decomposition and invariant moments. The skeleton was extracted from the gait feature image. Parameters of skeleton involving simplified model were extracted. These parameters, including invariant moments and skeleton, were given to support vector machines （SVM） for gait recognition. This method was applied to Tianjin University Infrared Gait Data-set （TIGD） and achieved recognition rate of 84 - 92%. Results proved that this method would benefit extracting the gait essential feature.