Benchmarking asymmetric 3D-2D face recognition systems

International audience— Asymmetric 3D-2D face recognition (FR) aims torecognize individuals from 2D face images using textured 3Dface models in the gallery (or vice versa). This new FR scenariohas the potential to be readily deployable in field applicationswhile still keeping the advantages of 3D FR solutions of beingmore robust to pose and lighting variations. In this paper, wepropose a new experimental protocol based on the UHDB11dataset for benchmarking 3D-2D FR algorithms. This newexperimental protocol allows for the study of the performanceof a 3D-2D FR solution under pose and/or lighting variations.Furthermore, we also benchmarked two state of the art 3D-2D FR algorithms. One is based on the Annotated DeformableModel (using manually labeled landmarks in this paper) usingmanually labeled landmarks whereas the other makes useof Oriented Gradient Maps along with an automatic poseestimation through random forest