融合奇异值分解和主分量分析的人脸识别算法

Fusion of SVD and PCA for face recognition

提出了奇异值分解(SVD)和主分量分析(PCA)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PCA之间有着明显的互补之处。PCA在图像表示上是最佳的(在均方差意义上),但敏感于位移、旋转等几何变换。而SVD则具有位移、旋转不变性。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PCA方法)。在ORL数据库上的实验表明,SVD和PCA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 principal component analysis (PCA) 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. One of drawbacks of PCA-based method is that PCA is sensitive to translation, rotation and other geometric transforms. Contrary to PCA, SVD has the merit of invariance to translation, rotation and other geometric transforms. By combining these two methods, 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.