Group emotion recognition in the wild by combining deep neural networks for facial expression classification and scene-context analysis

Abstract

This paper presents the implementation details of a proposed solution to the Emotion Recognition in the Wild 2017 Challenge, in the category of group-level emotion recognition. The objective of this sub-challenge is to classify a group's emotion as Positive, Neutral or Negative. Our proposed approach incorporates both image context and facial information extracted from an image for classification. We use Convolutional Neural Networks (CNNs) to predict facial emotions from detected faces present in an image. Predicted facial emotions are combined with scene-context information extracted by another CNN using fully connected neural network layers. Various techniques are explored by combining and training these two Deep Neural Network models in order to perform group-level emotion recognition. We evaluate our approach on the Group Affective Database 2.0 provided with the challenge. Experimental evaluations show promising performance improvements, resulting in approximately 37% improvement over the competition's baseline model on the validation dataset.