Papers › Group Affect Prediction Using Multimodal Distributions
Group Affect Prediction Using Multimodal Distributions
Saqib Shamsi, Bhanu Pratap Singh Rawat, Manya Wadhwa
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.
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