中文摘要：

针对人脸识别的特征提取问题,本文提出了一种张量正交局部敏感判别分析（Tensor-based Orthogonal Locality Sensitive Discriminant Analysis,Tensor-OLSDA）的人脸识别算法。张量正交局部敏感判别分析在保持了流形的局部几何结构的同时加强了全局判别结构,并克服了局部敏感判别分析算法中非正交性带来的度量失真和维数估计困难等问题,从而增强了数据的可分性,提高了识别效果。张量正交局部敏感判别分析首先将人脸数据表示成高阶张量形式,在进行特征提取时将高阶张量数据沿不同阶展开,再利用特征根之间的正交性约束条件,求解正交局部敏感判别式分析特征子空间,最后将高阶人脸数据投影于这个特征子空间,进行识别。在AT＆T和YaleB人脸库上的实验结果表明,Tensor-OLSDA具有良好的分类性能,能获得较为理想的识别结果。

英文摘要：

In this paper,a novel appearance-based feature extraction method called Tensor-based Orthogonal Locality Sensitive Discriminant Analysis（Tensor-OLSDA） is presented for feature extraction problem in face recognition.Tensor-OLSDA preserves the intrinsic local manifold structure and the geometrical information as well as strengthens the discriminant power.And it also overcomes the Metric distortion due to the non-orthogonality,which distorts the local geometrical structure of the data sub-manifold,and reduces the difficulty for dimension estimation,therefore,improves the separability of face data and gives a better recognition result.With high-order tensor representation of the face data,the extraction is made along each order of the unfold data and the feature subspace is obtained by OLSDA with orthogonal constraints.At last,the original face data is projected onto this feature subspace for recognition.Experiments based on the ATT and YaleB face database show the impressive classification capability of the proposed method.Experimental results show Tensor-OLSDA achieves the top average recognition rate in the several compared methods which also confirms that the locality preserving ability is enforced by computing the mutually orthogonal basis functions iteratively with tensor data representation.