Enhancing Face Recognition from Massive Weakly Labeled Data of New Domains

摘要

Training data are critical in face recognition systems. Labeling a large scale dataset for a particular domain needs lots of manpower. Without dataset related to current face recognition domain, wecan't get a strong face recognitionmodel with existing public datasets. In this paper, we propose a semi-supervised method to automatically construct strong dataset which can be trained to achieve better performance on the target domain frommassive weakly labeled data. In the case of Asian face recognition, a well trained VRCN model by CASIA, which achieves 98.63% on LFW and 91.76% on YTF, only achieves 88.53% recognition rate on our test dataset of Asian faces. We collect 530,560 weakly labeled Asian face images of 7962 identities, and get a cleaned dataset of size 285,933. Model trained by the cleaned dataset withVRCNnetwork and same strategy achieves 95.33% recognition rate on the Asian face test dataset (6.8% improved).