Discrimination Between Genuine Versus Fake Emotion Using Long-Short Term Memory with Parametric Bias and Facial Landmarks
Abstract
Discriminating between genuine and fake emotion is a new challenge because it is in contrast to the typical facial expression recognition that aims to classify the emotional state of a given facial stimulus. Fake emotion detection could be useful in telling how good an actor is in the movie or in judging a suspect tells the truth or not. To tackle this issue, we propose a new model by combining a mirror neuron modeling and deep recurrent networks, called long-short term memory (LSTM) with parametric bias (PB), by which features are extracted in the spatial-temporal domain from the facial landmarks, and then boil down to two PB vectors: one for genuine and other for fake one. Additionally, a binary classifier based on a gradient boosting is used to enhance discrimination capability between two PB vectors. The highest score from our system was 66.7 % in accuracy, suggesting that this approach could have a potential for useful applications.
Cite
Text
Kim and Huynh. "Discrimination Between Genuine Versus Fake Emotion Using Long-Short Term Memory with Parametric Bias and Facial Landmarks." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.362Markdown
[Kim and Huynh. "Discrimination Between Genuine Versus Fake Emotion Using Long-Short Term Memory with Parametric Bias and Facial Landmarks." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/kim2017iccvw-discrimination/) doi:10.1109/ICCVW.2017.362BibTeX
@inproceedings{kim2017iccvw-discrimination,
title = {{Discrimination Between Genuine Versus Fake Emotion Using Long-Short Term Memory with Parametric Bias and Facial Landmarks}},
author = {Kim, Yong-Guk and Huynh, Xuan-Phung},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {3065-3072},
doi = {10.1109/ICCVW.2017.362},
url = {https://mlanthology.org/iccvw/2017/kim2017iccvw-discrimination/}
}