Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
This work addresses emotion recognition in real-world images for applications like social media analysis, but it is incremental as it builds on existing methods.
The paper tackled group emotion recognition in unstructured environments by combining deep neural networks and Bayesian classifiers, achieving 64.68% accuracy on a test set, which outperformed the 53.62% baseline.
Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken. Some of the obstacles for an effective classification are occlusions, variable lighting conditions, and image quality. In this work we present a solution based on a novel combination of deep neural networks and Bayesian classifiers. The neural network works on a bottom-up approach, analyzing emotions expressed by isolated faces. The Bayesian classifier estimates a global emotion integrating top-down features obtained through a scene descriptor. In order to validate the system we tested the framework on the dataset released for the Emotion Recognition in the Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the test set, significantly outperforming the 53.62% competition baseline.