In this paper,a fast facial descriptor (FFD) algorithm and a face ima ges similarity score matching strategy are proposed to improve the uneven distribution of features and poor illumination robus tness o f speed up robust features (SURF) in describing features for single image face recognition.The integral image is reconstructed to increase the number of features on the lathy edge of face image to alleviate the problems of SURF.In order to reduce the number of r edundant features,the conception of distinction between the corresponding features is introduced to extract sparse features on t raining images.According to the contribution to recognition rate,face image regions are weighted to calculate the similarity sc ore between face images to conduct the final result. Experiments of single image based face recognition are conducted on AR,Yale B,CM U PIE databases and real-life ID card image database.The results indicate that the proposed method is robust to single imag e based face recognition under illumination,occlusion and expression changes,and the running time is only 0.042s.Compared wi th some other recent feature description methods, the proposed method can increase the recognition rate by up to 65%.Although the images in real-life ID card image database are greatly affected by actual environmental factors,the recognition rate is increa sed by 30%.