Florence faces: A dataset supporting 2D/3D face recognition

This article describes a new dataset under construction at the Media Integration and Communication Center and the University of Florence. The dataset consists of high-resolution 3D scans of human faces from each subject, along with several video sequences of varying resolution and zoom level. Each subject is recorded in a controlled setting in HD video, then in a less-constrained (but still indoor) setting using a standard, PTZ surveillance camera, and finally in an unconstrained, outdoor environment with challenging conditions. In each sequence the subject is recorded at three levels of zoom. This dataset is being constructed specifically to support research on techniques that bridge the gap between 2D, appearance-based recognition techniques, and fully 3D approaches. It is designed to simulate, in a controlled fashion, realistic surveillance conditions and to probe the efficacy of exploiting 3D models in real scenarios.