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Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network

CVPR 2018 · Architectures and efficiency › CNN design · approach: Dense matching and estimation CNNs · 100 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add PCA-based 2D shape-model fitting to the network heatmaps (ablation)Local-global context network · iBUGmedian IOD-normalized MAE: 5.55 -> 5.29 / 4.36 -> 4.18Helped
add PCA-based 2D shape-model fitting to the network heatmaps (ablation)Local-global context network · LFPW + HELENmedian IOD-normalized MAE: 3.04 -> 2.86 / 2.34 -> 2.21Helped
add PCA-based 2D shape-model fitting to the network heatmaps (ablation)Local-global context network · Menpo frontalmedian image-size-normalized MAE: 2.27 -> 2.14 / 1.90 -> 1.79Helped
replace square-error heatmap loss with pixel-wise cross-entropy loss (ablation)Local-global context network · 300-Wlandmark detection performanceHurt
replace square-error heatmap loss with pixel-wise cross-entropy loss (ablation)Local-global context network · Kaggle facial keypoint challengelandmark detection performanceMixed
use RGB or HSV input instead of grayscale (ablation)Local-global context network · Facial landmark detection experimentslandmark detection performanceNo clear effect
replace dilated convolutions with deformable convolutions (ablation)Global-context subnet · Facial landmark detection experimentsperformanceNo clear effect

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