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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In our work, we present a novel local detector -- Convolutional Experts Network (CEN) -- that brings together the advantages of neural architectures and mixtures of experts in an end-to-end framework.
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Ranked #4 on Semantic Segmentation on LIP val
In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes.
By utilising boundary information of 300-W dataset, our method achieves 3. 92% mean error with 0. 39% failure rate on COFW dataset, and 1. 25% mean error on AFLW-Full dataset.
Ranked #7 on Face Alignment on WFLW
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-Front (Mean NME metric)
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 Facial Landmark Detection on 300W (Mean Error Rate metric)