1 code implementation • Pattern Recognition 2024 • Alejandro Cobo, Roberto Valle, José M. Buenaposada, Luis Baumela
We also propose a generalization of the geodesic angular distance metric that enables the construction of a loss that controls the contribution of each training sample to the optimization of the model.
Ranked #1 on Head Pose Estimation on Panoptic
1 code implementation • 22 Dec 2020 • Roberto Valle, José Miguel Buenaposada, Luis Baumela
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 COFW (Recall at 80% precision (Landmarks Visibility) metric)
1 code implementation • Pattern Recognition Letters 2019 • Roberto Valle, Jose M. Buenaposada, Luis Baumela
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 Face Alignment on COFW (NME (inter-pupil) metric)
1 code implementation • 5 Feb 2019 • Roberto Valle, José M. Buenaposada, Antonio Valdés, Luis Baumela
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 Face Alignment on 300W Split 2 (NME (inter-ocular) metric)
1 code implementation • ECCV 2018 • Roberto Valle, Jose M. Buenaposada, Antonio Valdes, Luis Baumela
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 300W Split 2 (FR@8 (inter-ocular) metric)