Group-level Emotion Recognition using Transfer Learning from Face Identification
This work addresses emotion recognition in group settings for applications like social media analysis, but it is incremental as it adapts existing methods to a specific challenge.
The paper tackled group-level emotion recognition by using transfer learning from face identification features and an ensemble of Random Forest classifiers, achieving 75.4% accuracy on validation data, which is 20% higher than a handcrafted feature-based baseline.
In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems. In the final pipeline an ensemble of Random Forest classifiers was learned to predict emotion score using available training set. In case when the faces have not been detected, one member of our ensemble extracts features from the whole image. During our experimental study, the proposed approach showed the lowest error rate when compared to other explored techniques. In particular, we achieved 75.4% accuracy on the validation data, which is 20% higher than the handcrafted feature-based baseline. The source code using Keras framework is publicly available.