Multi-View Facial Expression Recognition Analysis with Generic Sparse Coding Feature
Abstract
Expression recognition from non-frontal faces is a challenging research area with growing interest. This paper works with a generic sparse coding feature, inspired from object recognition, for multi-view facial expression recognition. Our extensive experiments on face images with seven pan angles and five tilt angles, rendered from the BU-3DFE database, achieve state-of-the-art results. We achieve a recognition rate of 69.1% on all images with four expression intensity levels, and a recognition performance of 76.1% on images with the strongest expression intensity. We then also present detailed analysis of the variations in expression recognition performance for various pose changes.
Cite
Text
Tariq et al. "Multi-View Facial Expression Recognition Analysis with Generic Sparse Coding Feature." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33885-4_58Markdown
[Tariq et al. "Multi-View Facial Expression Recognition Analysis with Generic Sparse Coding Feature." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/tariq2012eccv-multi/) doi:10.1007/978-3-642-33885-4_58BibTeX
@inproceedings{tariq2012eccv-multi,
title = {{Multi-View Facial Expression Recognition Analysis with Generic Sparse Coding Feature}},
author = {Tariq, Usman and Yang, Jianchao and Huang, Thomas S.},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {578-588},
doi = {10.1007/978-3-642-33885-4_58},
url = {https://mlanthology.org/eccv/2012/tariq2012eccv-multi/}
}