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Datasets/MERL-RAV

MERL-RAV

MERL Reannotation of AFLW with Visibility

ImagesIntroduced 2020-07-21

The MERL-RAV (MERL Reannotation of AFLW with Visibility) Dataset contains over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded (due to extreme head poses), or externally occluded. The images were annotated by professional labelers, supervised by researchers at Mitsubishi Electric Research Laboratories (MERL).

Benchmarks

1 Image, 2*2 Stitchi/MAE mean (º)1 Image, 2*2 Stitchi/MAE yaw (º)1 Image, 2*2 Stitchi/MAE pitch (º)1 Image, 2*2 Stitchi/MAE roll (º)3D/MAE mean (º)3D/MAE yaw (º)3D/MAE pitch (º)3D/MAE roll (º)3D/NME (box)3D/AUC@7 (box) 3D Face Modelling/NME (box)3D Face Modelling/AUC@7 (box) 3D Face Reconstruction/NME (box)3D Face Reconstruction/AUC@7 (box) Face Reconstruction/NME (box)Face Reconstruction/AUC@7 (box) Facial Recognition and Modelling/NME (box)Facial Recognition and Modelling/AUC@7 (box) Pose Estimation/MAE mean (º)Pose Estimation/MAE yaw (º)Pose Estimation/MAE pitch (º)Pose Estimation/MAE roll (º)

Statistics

Papers
4
Benchmarks
22

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Tasks

1 Image, 2*2 Stitchi3D3D Face Modelling3D Face ReconstructionFace AlignmentFace ReconstructionFacial Recognition and ModellingPose Estimation