基于步态的身份识别

提出了一种简单有效的自动步态识别算法，对于每个序列而言，一种改进的背景减除方法用于检测行人的运动轮廓，然后，这些时变的2D轮廓形状被转换为对应的1D距离信号，同时通过特征空间变换来提取低维步态特征，基于时空相关或归一化欧氏距离度量，标准的模式分类技术用于最终的识别，实验结果表明该算法不仅获得了令人鼓舞的识别性能，而且拥有相对较低的计算代价。

Gait-Based Human Identification

This paper proposes a simple and efficient motion based gait recognition algorithm by spatial temporal silhouette analysis. For each image sequence, an improved background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure from the background. Then, eigenspace transformation based on the traditional principal component analysis (PCA) is applied to time varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower dimensional eigenspace for recognition. This method can implicitly capture the structural and transitional characteristics of gait, especially biometric shape cues. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively lower computational cost.