基于PCA和IGG权函数的人脸图像鲁棒线性回归分类方法

Face recognition using robust linear regression classification based on PCA and IGG weight function

在人脸识别中,线性回归分类是一种快速且有效的方法。然而,线性回归分类是基于图像向量进行识别,导致原始矩阵图像往往为高维数据,且人脸图像经常受到污染。为此,提出一种基于PCA和IGG权函数的鲁棒线性回归分类方法。首先通过PCA对人脸图像进行降维,再采用IGG权函数对被污染的人脸图像进行分类。选用公开的ORL和Yale人脸库,将线性回归分类、基于IGG权函数的鲁棒线性回归分类和基于PCA和IGG权函数的鲁棒线性回归分类进行比较。实验结果表明,在ORL和Yale人脸库中,在不加噪声和加入椒盐噪声和斑点噪声条件下,所提出方法的识别率均值都在92.07%以上,均高于另外两种方法。

Linear regression classification is a fast and effective method in face recognition.However,linear regression classification is based on image vector recognition,which leads to the fact that the original matrix image is often high-dimensional data,and the face image is often contaminated.In order to solve this problem,a robust linear regression classification algorithm based on PCA and IGG weight function is proposed in this paper.Firstly,PCA is used to reduce the dimensionality of the face image,then the IGG weight function is adopted to classify the contaminated face image.Linear regression classification,robust linear regression classification based on IGG weight function and robust linear regression classification based on PCA and IGG weight function methods are compared with the public ORL and Yale databases.The experimental results show that the average recognition rate of the proposed method is above 92.07%without noise and with salt and pepper noise and speckle noise,which are higher than the other two methods in the ORL and Yale databases.