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.