Towards Facial Expression Recognition in the Wild: A New Database and Deep Recognition System
Abstract
Automatic facial expression recognition (FER) plays an important role in many fields. However, most existing FER techniques are devoted to the tasks in the constrained conditions, which are different from actual emotions. To simulate the spontaneous expression, the number of samples in acted databases is usually small, which limits the ability of facial expression classification. In this paper, a novel database for natural facial expression is constructed leveraging the social images and then a deep model is trained based on the naturalistic dataset. An amount of social labeled images are obtained from the image search engines by using specific keywords. The algorithms of junk image cleansing are then utilized to remove the mislabeled images. Based on the collected images, the deep convolutional neural networks are learned to recognize these spontaneous expressions. Experiments show the advantages of the constructed dataset and deep approach.
Cite
Text
Peng et al. "Towards Facial Expression Recognition in the Wild: A New Database and Deep Recognition System." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016. doi:10.1109/CVPRW.2016.192Markdown
[Peng et al. "Towards Facial Expression Recognition in the Wild: A New Database and Deep Recognition System." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016.](https://mlanthology.org/cvprw/2016/peng2016cvprw-facial/) doi:10.1109/CVPRW.2016.192BibTeX
@inproceedings{peng2016cvprw-facial,
title = {{Towards Facial Expression Recognition in the Wild: A New Database and Deep Recognition System}},
author = {Peng, Xianlin and Xia, Zhaoqiang and Li, Lei and Feng, Xiaoyi},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2016},
pages = {1544-1550},
doi = {10.1109/CVPRW.2016.192},
url = {https://mlanthology.org/cvprw/2016/peng2016cvprw-facial/}
}