Facial Expression Recognition with Deep Learning

TL;DR

This paper develops and compares deep learning models for facial expression recognition, achieving state-of-the-art accuracy and demonstrating real-time application on mobile devices.

Contribution

It introduces a high-accuracy FER model leveraging recent techniques and presents a real-time mobile web app for practical deployment.

Findings

75.8% accuracy on FER2013 test set

Outperforms existing FER models

Real-time on-device facial expression recognition

Abstract

One of the most universal ways that people communicate is through facial expressions. In this paper, we take a deep dive, implementing multiple deep learning models for facial expression recognition (FER). Our goals are twofold: we aim not only to maximize accuracy, but also to apply our results to the real-world. By leveraging numerous techniques from recent research, we demonstrate a state-of-the-art 75.8% accuracy on the FER2013 test set, outperforming all existing publications. Additionally, we showcase a mobile web app which runs our FER models on-device in real time.