关键词: 测地距离, 张量边界Fisher分析, 流形学习, 降维, 人脸识别

Abstract: Face exists in the way of low-dimensional manifold which is embedded in the high dimensional observation space. In order to describe the fine structure of face space more accurately, a way of tensor analysis of the boundary fisher face recognition which is based on local geodesic distance is proposed. It adopts the two-dimensional tensor to show the image samples and local geodesic distance, and calculates the nearest points of samples. This method can reveal the inner manifold geometry and select the similar and heterogeneous data points which exist in the manifold more accurately while avoiding the small sample problem. The experiments which are tested on the PIE and the FERET face database show that this method can achieve higher recognition rate and verify its effectiveness.

Key words: geodesic distance, Tensor Marginal Fisher Analysis（TMFA）, manifold learning, dimensionality reduction, face recognition