Abstract:

Most methods of face recognition use amounts of corrected labeled samples to learn recognition models with high curate.Collecting face images and labeling them will consume plenty of manpower.In order to label the possession images,researchers have done many works and have made many contributions,but due to personal reason,the labels may be not correct entirely,we call the incorrect labels class noise.The paper is aimed at face recognition with class noise,point that SVM suit for these problems and explain the reason why SVM is robust to noise according to influence of support vectors' position to classification.Discarding certain samples which were judged as noise based on SVM improves the robustness.Amounts of experiences in PubFig dataset verify the efficiency of SVM and the improvement algorithm in noisy learning.

Key words: face recognition;noise learning;SVM