Face Alignment On Cofw

Metrics

NME (inter-ocular)

Results

Performance results of various models on this benchmark

Model Name	NME (inter-ocular)	Paper Title	Repository
HRNet	3.45	Deep High-Resolution Representation Learning for Visual Recognition
SLPT	3.32	Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning
Wing (Feng et al., 2018)	5.07	Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
PIPNet (ResNet-101)	3.08%	Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
DenseU-Net + Dual Transformer	-	Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
DTLD+	3.02%	Towards Accurate Facial Landmark Detection via Cascaded Transformers	-
MobileNetV2+KD-Loss	4.11%	Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks	-
CHR2C (Inter-pupils Norm)	-	Cascade of Encoder-Decoder CNNs with Learned Coordinates Regressor for Robust Facial Landmarks Detection
LAB (w/ B)	3.92%	Look at Boundary: A Boundary-Aware Face Alignment Algorithm
LAB	5.58%	Look at Boundary: A Boundary-Aware Face Alignment Algorithm
Ours (VGG-F)	3.32	Pre-training strategies and datasets for facial representation learning
ATF	3.32%	ATF: Towards Robust Face Alignment via Leveraging Similarity and Diversity across Different Datasets	-
MNN (Inter-pupil Norm)	-	Multi-task head pose estimation in-the-wild
PropNet	3.71%	PropagationNet: Propagate Points to Curve to Learn Structure Information	-
STAR	3.21%	STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
BarrelNet (ResNet-101)	3.1%	When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model	-
FiFA	2.96	Fiducial Focus Augmentation for Facial Landmark Detection	-
EF-3ACR	3.47%	ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment	-
SCC	3.63%	Fast and Accurate: Structure Coherence Component for Face Alignment	-
DCFE	-	A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment	-

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