Using 3D Models to Recognize 2D Faces in the Wild

In this paper we consider the problem of face recognition in imagery captured in uncooperative environments using PTZ cameras. For each subject enrolled in the gallery, we acquire a high-resolution 3D model from which we generate a series of rendered face images of varying viewpoint. The result of regularly sampling face pose for all subjects is a redundant basis that over represents each target. To recognize an unknown probe image, we perform a sparse reconstruction of SIFT features extracted from the probe using a basis of SIFT features from the gallery. While directly collecting images over varying pose for all enrolled subjects is prohibitive at enrollment, the use of high speed, 3D acquisition systems allows our face recognition system to quickly acquire a single model, and generate synthetic views offline. Finally we show, using two publicly available datasets, how our approach performs when using rendered gallery images to recognize 2D rendered probe images and 2D probe images acquired using PTZ cameras.