We contribute with a network architecture and training strategy that harness the strong dependencies among face pose, alignment and visibility, to produce a top performing model for all three tasks.
Ranked #1 on Face Alignment on AFLW2000-3D
The advent of a panoply of resource limited devices opens up new challenges in the design of computer vision algorithms with a clear compromise between accuracy and computational requirements.
Detecting local features, such as corners, segments or blobs, is the first step in the pipeline of many Computer Vision applications.
Ranked #8 on Line Segment Detection on York Urban Dataset
In this paper we investigate the use of a cascade of Neural Net regressors to increase the accuracy of the estimated facial landmarks.
Ranked #4 on Facial Landmark Detection on 300W
In this paper we present 3DDE, a robust and efficient face alignment algorithm based on a coarse-to-fine cascade of ensembles of regression trees.
Ranked #2 on Facial Landmark Detection on AFLW-Full (Mean NME metric)
In this paper we present DCFE, a real-time facial landmark regression method based on a coarse-to-fine Ensemble of Regression Trees (ERT).
Ranked #2 on Face Alignment on IBUG