提出了一种基于张量子空间学习降维人体高维侧影数据的人行为识别方法。给定一个动作的人侧影图像序列，首先用张量子空间学习方法将目标高维侧影图像投影到低维子空间来描述人运动的时空特性，并同时尽可能地保持目标侧影图像中像素之间的空间几何信息，然后用Hausdorff距离度量动作之间的相似性，并在最近邻距离框架下对动作进行分类识别。为验证本文算法的有效性，设计了动作识别和鲁棒性测试2个实验。实验结果表明提出的算法不仅能够有效地对人行为进行识别，且具有较强的鲁棒性。

In this paper, a simple but efficient algorithm based on tensor subspace learning is proposed to reduce the dimensionality of high-dimensional silhouette data for human action recognition. For image sequences of each action, they are projected into a low dimensional subspace so that both spatial and temporal properties of the action are preserved. Further, a nearest-neighbor action recognition is carried out basing on Hausdorff distance. Two experiments for action recognition and robust test have been carried out to testify the effectiveness of introduced tensor subspace learning.