Probabilistic Temporal Head Pose Estimation Using a Hierarchical Graphical Model
Abstract
We present a hierarchical graphical model to probabilistically estimate head pose angles from real-world videos, that leverages the temporal pose information over video frames. The proposed model employs a number of complementary facial features, and performs feature level, probabilistic classifier level and temporal level fusion. Extensive experiments are performed to analyze the pose estimation performance for different combination of features, different levels of the proposed hierarchical model and for different face databases. Experiments show that the proposed head pose model improves on the current state-of-the-art for the unconstrained McGillFaces [10] and the constrained CMU Multi-PIE [14] databases, increasing the pose classification accuracy compared to the current top performing method by 19.38% and 19.89%, respectively.
Cite
Text
Demirkus et al. "Probabilistic Temporal Head Pose Estimation Using a Hierarchical Graphical Model." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10590-1_22Markdown
[Demirkus et al. "Probabilistic Temporal Head Pose Estimation Using a Hierarchical Graphical Model." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/demirkus2014eccv-probabilistic/) doi:10.1007/978-3-319-10590-1_22BibTeX
@inproceedings{demirkus2014eccv-probabilistic,
title = {{Probabilistic Temporal Head Pose Estimation Using a Hierarchical Graphical Model}},
author = {Demirkus, Meltem and Precup, Doina and Clark, James J. and Arbel, Tal},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {328-344},
doi = {10.1007/978-3-319-10590-1_22},
url = {https://mlanthology.org/eccv/2014/demirkus2014eccv-probabilistic/}
}