Emotion recognition with 4kresolution database

TL;DR

This paper introduces a new 4K resolution in-the-wild facial emotion database and evaluates various neural network models on it, comparing performance to the existing Aff-wild database.

Contribution

The creation of a high-resolution 4K facial emotion database and the assessment of multiple neural network models trained on this dataset.

Findings

Higher resolution improves emotion recognition accuracy.

ResNet outperforms other models on the 4K database.

Comparison shows the impact of resolution on model performance.

Abstract

Classifying the human emotion through facial expressions is a big topic in both the Computer Vision and Deep learning fields. Human emotion can be classified as one of the basic emotion types like being angry, happy or dimensional emotion with valence and arousal values. There are a lot of related challenges in this topic, one of the most famous challenges is called the 'Affect-in-the-wild Challenge'(Aff-Wild Challenge). It is the first challenge on the estimation of valence and arousal in-the-wild. This project is an extension of the Aff-wild Challenge. Aff-wild database was created using images with a mean resolution of 607*359, I and Dimitrios sought to find out the performance of the model that is trained on a database that contains4K resolution in-the-wild images. Since there is no existing database to satisfy the requirement, I built this database from scratch with help from Dimitrios and trained neural network models with different hyperparameters on this database. I used network models likeVGG16, AlexNet, ResNet and also some pre-trained models like Ima-geNet VGG. I compared the results of the different network models alongside the results from the Aff-wild database to exploit the optimal model for my database.