Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding
Abstract
Improved dense trajectory features have been successfully used in video-based action recognition problems, but their application to face processing is more challenging. In this paper, we propose a novel system that deals with the problem of emotion recognition in real-world videos, using improved dense trajectory, LGBP-TOP, and geometric features. In the proposed system, we detect the face and facial landmarks from each frame of a video using a combination of two recent approaches, and register faces by means of Procrustes analysis. The improved dense trajectory and geometric features are encoded using Fisher vectors and classification is achieved by extreme learning machines. We evaluate our method on the extended Cohn-Kanade (CK+) and EmotiW 2015 Challenge databases. We obtain state-of the-art results in both databases.
Cite
Text
Afshar and Salah. "Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016. doi:10.1109/CVPRW.2016.189Markdown
[Afshar and Salah. "Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016.](https://mlanthology.org/cvprw/2016/afshar2016cvprw-facial/) doi:10.1109/CVPRW.2016.189BibTeX
@inproceedings{afshar2016cvprw-facial,
title = {{Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding}},
author = {Afshar, Sadaf and Salah, Albert Ali},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2016},
pages = {1517-1525},
doi = {10.1109/CVPRW.2016.189},
url = {https://mlanthology.org/cvprw/2016/afshar2016cvprw-facial/}
}