针对传统基于向量子空间降维的图像匹配算法易丢失像素间邻域关系和计算量大的问题，提出一种基于张量子空间降维的边缘图像匹配算法。通过双边投影变换提取边缘图像的张量子空间，在降低特征空间维数的同时保持边缘特征之间的邻域关系，同时采用边缘膨胀后的互相关度量模板与实时图的相似性。标准人脸数据库和红外实时图像的匹配实验结果表明：该算法在匹配时间、匹配正确率、匹配精度3方面较传统基于向量子空间的匹配算法有显著的性能提高，并且对杂波和部分遮挡有较强的适应性。

An image matching algorithm based on tensor subspace dimensionality reduction was proposed to the questions of easily losing relationships between pixels and intensively computational problems using traditional vector subspace methods. The algorithm extracts tensor subspace by employing two-sided projection transformation in edge images, reducing dimension of feature space and preserving the relationships between edge pixels. The algorithm measured the similarity between template and real-time image by calculating the correlation of dilated binary images. Experimental results on the standard face database and real IR images show that the new algorithm can improve the computational efficiency remarkably，and has a higher matching rate and matching precision than traditional vector subspace methods. The proposed algorithm can also be applied in cluttering and partially occluded circumstances.