Facial Expression Recognition Using Encoded Dynamic Features
Abstract
In this paper, we propose a novel framework for video-based facial expression recognition, which can handle the data with various time resolution including a single frame. We first use the haar-like features to represent facial appearance, due to their simplicity and effectiveness. Then we perform K-Means clustering on the facial appearance features to explore the intrinsic temporal patterns of each expression. Based on the temporal pattern models, we further map the facial appearance variations into dynamic binary patterns. Finally, boosting learning is performed to construct the expression classifiers. Compared to previous work, the dynamic binary patterns encode the intrinsic dynamics of expression, and our method makes no assumption on the time resolution of the data. Extensive experiments carried on the Cohn-Kanade database show the promising performance of the proposed method.
Cite
Text
Yang et al. "Facial Expression Recognition Using Encoded Dynamic Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587717Markdown
[Yang et al. "Facial Expression Recognition Using Encoded Dynamic Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/yang2008cvpr-facial/) doi:10.1109/CVPR.2008.4587717BibTeX
@inproceedings{yang2008cvpr-facial,
title = {{Facial Expression Recognition Using Encoded Dynamic Features}},
author = {Yang, Peng and Liu, Qingshan and Cui, Xinyi and Metaxas, Dimitris N.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587717},
url = {https://mlanthology.org/cvpr/2008/yang2008cvpr-facial/}
}