PFW: A Face Database in the Wild for Studying Face Identification and Verification in Uncontrolled Environment

To train and evaluate various face recognition algorithms, quite many databases have been created. But most of them have been created under controlled conditions to study the specific variations of the face recognition problem. These variations include position, pose, lighting, background, camera quality and gender. But in real environment, there are also many applications in which there is little or no control over such variations. Labeled Faces in the Wild, a database has been provided to study the latter, unconstrained face recognition problem. However, LFW is proposed for face verification problem, while we observe that a good verification performance cannot guarantee a good identification performance in real situation. Further, the face images in LFW are not sufficient for training to get a state of the art performance. PFW, POS Faces in the Wild, on the contrast, is a large database which can be served both for evaluating face verification and face identification algorithms. Specifically, PFW contains a certain number of identities and each identity contains quite many images, thus make it suitable both for large scale supervised and semi supervised training. In this paper, we also provide some rules for evaluating the identification algorithm performance in real environment. To the best of our knowledge, our database is the first public available large face data set proposed for face identification in unconstrained environment.