融合奇异值分解和线性鉴别分析的人脸识别算法

Fusion of SVD and LDA for face recognition

本文提出了奇异值分解(SVD)和线性鉴别分析(LDA)相结合的人脸识别算法。理论上,当两种数据或分类器具有一定的独立性或互补性时,数据融合或分类器融合才能改善识别率。SVD和LDA之间有着明显的互补之处,LDA在fisher准则下能最大限度地把不同的类别区分开来,但作为一种子空间方法,LDA敏感于位移、旋转等几何变换。而作为一种代数特征提取方法的SVD则具有位移、旋转不变性等优点。因此,将这两种方法相结合就有可能提高分类性能(好于单独的SVD方法和单独的LDA方法)。在ORL数据库上的实验表明,SVD和LDA相融合的识别方法的确提高了人脸识别率。

A face recognition method based on the fusion of linear discriminant analysis （LDA） and singular value decomposition （SVD） is presented. In theory, fusion of different data or classifiers can achieve better performance when they are independent or they can overcome the shortcomings of each other. As one of the subspace methods, LDA-based method has a drawback that LDA is sensitive （variant） to translation, rotation and other geometric transforms. Contrary to LDA, SVD has a merit of invariance to translation, rotation and other geometric transforms. By combining these two methodsm it is expected that better recognition performance can be obtained. Experiment results on ORL face database demonstrate that the proposed method can indeed improve face recognition rate.