基于PCA的人脸识别方法的比较研究

主成成份分析(PCA)方法是人脸识别技术中常用的一种一维特征抽取方法。传统PCA方法用于人脸识别常常面临图像维数高,直接计算量的问题。为了解决这2个问题,人们对PCA进行了改进,提出并实现了多种基于PCA的人脸识别。对3种基于PCA的人脸识别方法做了理论上的研究和实验上的性能比较。实验结果表明PCA 2DPCA是其中综合效果最好的一种方法。

A Comparative Study on Face Recognition Based on PCA

Principal Component Analysis(PCA) is a common feature extraction method based on 1D vector.But it often meets two problems as high dimensions and big computation quantity when applying to face recognition.In order to solve the two problems,many improved methods are proposed and realized.This paper compares three of them on theory and experimental data.The experimental results show that PCA 2DPCA is the best of them.