Benchmarking Head Pose Estimation In-the-Wild

Data

• Poster
• Code
• AFLW

Abstract

Head pose estimation systems have quickly evolved from simple classifiers estimating a few yaw angles, to the most recent regression approaches that provide precise 3D face orientations in images acquired “in-the-wild”. Accurate evaluation of these algorithms is an open issue. Although the most recent approaches are tested using a few challenging annotated databases, their published results are not comparable. In this paper we review these works, define a common evaluation methodology, and establish a new state-of-the-art for this problem.

Figure 3: Representative results with yaw errors greater than 15 degrees for AFLW

Citation

Elvira Amador and Roberto Valle and José Miguel Buenaposada and Luis Baumela. Benchmarking Head Pose Estimation in-the-Wild. Iberoamerican Congress on Pattern Recognition 10657: 45-52 (2017)

@inproceedings{Amador17,
author = {Elvira Amador and Roberto Valle and Jos{\'{e}} Miguel Buenaposada and Luis Baumela},
title = {Benchmarking Head Pose Estimation in-the-Wild},
booktitle = {Iberoamerican Congress on Pattern Recognition},
volume = {10657},
pages = {45-52},
year = {2017},
url = {https://doi.org/10.1007/978-3-319-75193-1_6}
}