UHDB11 Database for 3D-2D Face Recognition

Performance boosts in face recognition have been facilitated by the formation of facial databases, with collection protocols customized to address challenges such as light variability, expressions, pose, sensor/modality differences, and, more recently, uncontrolled acquisition conditions. In this paper, we present database UHDB11, to facilitate 3D-2D face recognition evaluations, where the gallery has been acquired using 3D sensors (3D mesh and texture) and the probes using 2D sensors (images). The database consists of samples from 23 individuals, in the form of 2D high-resolution images spanning six illumination conditions and 12 head-pose variations, and 3D facial mesh and texture. It addresses limitations regarding resolution, variability and type of 3D/2D data and has demonstrated to be statistically more challenging, diverse and information rich than existing cohorts of 10 times larger number of subjects. We propose a set of 3D-2D experimental configurations, with frontal 3D galleries and pose-illumination varying probes and provide baseline performance for identification and verification (available at http://cbl.uh.edu/URxD/datasets).