Facial landmark detection is the task of detecting key landmarks on the face and tracking them (being robust to rigid and non-rigid facial deformations due to head movements and facial expressions).
( Image credit: Style Aggregated Network for Facial Landmark Detection )
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The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Ranked #3 on Semantic Segmentation on LIP val
A typical approach is to (1) train a detector on the labeled images; (2) generate new training samples using this detector's prediction as pseudo labels of unlabeled images; (3) retrain the detector on the labeled samples and partial pseudo labeled samples.
Ranked #1 on Facial Landmark Detection on 300W (Full) (using extra training data)
In this work, we propose a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection.
Ranked #1 on Facial Landmark Detection on AFLW-Full
In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video.
Ranked #1 on Facial Landmark Detection on 300-VW (C)
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Ranked #1 on Face Identification on IJB-B
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Ranked #1 on 3D Facial Expression Recognition on 2017_test set (using extra training data)
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.