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Group emotion recognition in the wild is a challenging problem, due to theunstructured environments in which everyday life pictures are taken. Some ofthe obstacles for an effective classification are occlusions, variable lightingconditions, and image quality. In this work we present a solution based on anovel combination of deep neural networks and Bayesian classifiers. The neuralnetwork works on a bottom-up approach, analyzing emotions expressed by isolatedfaces. The Bayesian classifier estimates a global emotion integrating top-downfeatures obtained through a scene descriptor. In order to validate the systemwe tested the framework on the dataset released for the Emotion Recognition inthe Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the testset, significantly outperforming the 53.62% competition baseline.

Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers