How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

ICCV 2017 Adrian BulatGeorgios Tzimiropoulos

This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Face Alignment 300-VW (C) 2D-FAN AUC0.07 64.1% # 1
Head Pose Estimation BIWI FAN (12 points) MAE 7.882 # 5
Face Alignment LS3D-W Balanced 3D-FAN AUC0.07 72.3% # 1

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Head Pose Estimation AFLW2000 FAN (12 points) MAE 9.116 # 5
Face Alignment AFLW2000 FAN (12 points) Error rate 8.714 # 4

Methods used in the Paper


METHOD TYPE
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