中文摘要：

步态识别在生物识别中研究日益增多。目前对步态的识别研究大都是考虑单一条件下步态的识别率,但在穿外套、背包等混合条件下识别率较低,该文分析了人体行走时步态的时序特征,提出一种4层的双尺度多信息融合的动态贝叶斯网络。模型中每个时间片都为整体信息即大尺度信息和局部细节信息即小尺度信息的融合。此模型能很好地表达步态的时序特性,即步态行走时人体姿态,运动幅度等特征的节奏性变化。实验结果表明该方法有较高的识别率,能有机地融合步态的整体信息及局部细节信息,并且在有轮廓噪声及信息缺失的情况下有较好的鲁棒性,大大降低了外套及背包对步态识别的影响。

英文摘要：

Gait recognition research gets rapid development as one of biometric.Now almost all the gait recognition researcher focus on gait recognition rate only in the single condition,but the gait recognition rate has rapid decline in the wearing coat and carrying bag condition.Based on analyzing the gait timing characteristics when human is moving,a novel gait recognition model that expressed two-scale dynamic Bayesian network and more information fusion is proposed.The model contains four levels of states and every time slice of the model is expressed by the fusion of large-scale information and small-scale information.This model can well express the timing characteristics of gait,that are the body posture and range of motion and other gait rhythmic changes characteristics.Experimental result show that the model recognizes gait with high rates and good robustness to the silhouette noise and lost of information and fuse the large-scale information and small-scale information well.The model can greatly reduce impact of gait recognition rate in the wearing coat and carrying bag condition.