Group-Level Emotion Recognition using Transfer Learning from Face Identification

P. 544–548.

Alexandr Rassadin, Alexey Gruzdev, Andrey Savchenko

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 to be made publicly available.

     

 

Keywords: распознавание эмоций, EmotiW 2017, Transfer Learning, Facial Expression Analysis, Group-level Emotion Recognition, Emotion Recognition in the Wild, Convolutional Neural Network, сверточные нейронные сети

Publication based on the results of: Разработка и апробация эффективных методов классификации для больших баз мультимедийных данных (2017)

In book

Proceedings of the 19th ACM International Conference on Multimodal Interaction

[б.и.], 2017.