Group Affect Prediction Using Multimodal Distributions
This work addresses emotion recognition for groups in images, but it is incremental as it builds on existing datasets and methods with a modest improvement.
The paper tackled group emotion prediction in images by proposing a CNN trained on emotion heatmaps, which outperformed a CNN trained on raw images, achieving a validation accuracy of 55.23%, 2.44% above the baseline.
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.