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).
|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
#2 best model for 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.
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.
SOTA for 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.
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.
At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation.
Facial landmark detection, or face alignment, is a fundamental task that has been extensively studied.
SOTA for Face Alignment on WFLW
Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision.
This Estimation-Correction-Tuning process perfectly combines the advantages of the global robustness of data-driven method (FCN), outlier correction capability of model-driven method (PDM) and non-parametric optimization of RLMS.