Group Affect Prediction Using Multimodal Distributions
Computer Vision and Pattern Recognition
2018-03-13 v2
Abstract
We describe our approach towards building an efficient predictive model to detect emotions for a group of people in an image. We have proposed that training a Convolutional Neural Network (CNN) model on the emotion heatmaps extracted from the image, outperforms a CNN model trained entirely on the raw images. The comparison of the models have been done on a recently published dataset of Emotion Recognition in the Wild (EmotiW) challenge, 2017. The proposed method achieved validation accuracy of 55.23% which is 2.44% above the baseline accuracy, provided by the EmotiW organizers.
Cite
@article{arxiv.1710.01216,
title = {Group Affect Prediction Using Multimodal Distributions},
author = {Saqib Shamsi and Bhanu Pratap Singh Rawat and Manya Wadhwa},
journal= {arXiv preprint arXiv:1710.01216},
year = {2018}
}
Comments
This research paper has been accepted at Workshop on Computer Vision for Active and Assisted Living, WACV 2018