Pose and Expression Robust Age Estimation via 3D Face Reconstruction from a Single Image
Abstract
In this paper, we present a deep learning architecture that exploits 3D face reconstruction to obtain a robust age estimation. To this end, effective representation is learned through an expression-, pose-, illumination-, reflectance-, and geometry-aware deep model reconstructing a 3D face from a single 2D image. The 3D face reconstruction network is combined with an appearance-based age estimation network, where the face reconstruction features are jointly learned with the visual ones. Experiments on large-scale datasets show that our method obtains promising results and outperforms state-of-the-art methods, especially in the presence of strong expressions and large pose variations. Furthermore, cross-dataset experiments show that the proposed method is able to generalize more effectively as opposed to the state-of-the-art methods.
Cite
Text
Savov et al. "Pose and Expression Robust Age Estimation via 3D Face Reconstruction from a Single Image." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00160Markdown
[Savov et al. "Pose and Expression Robust Age Estimation via 3D Face Reconstruction from a Single Image." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/savov2019iccvw-pose/) doi:10.1109/ICCVW.2019.00160BibTeX
@inproceedings{savov2019iccvw-pose,
title = {{Pose and Expression Robust Age Estimation via 3D Face Reconstruction from a Single Image}},
author = {Savov, Nedko and Ngô, Minh and Karaoglu, Sezer and Dibeklioglu, Hamdi and Gevers, Theo},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {1270-1278},
doi = {10.1109/ICCVW.2019.00160},
url = {https://mlanthology.org/iccvw/2019/savov2019iccvw-pose/}
}