Facial Expressions Recognition with Convolutional Neural Networks

TL;DR

This paper explores facial expression recognition using CNNs, achieving a 70.10% accuracy on FER2013 without extra data, highlighting deep learning's potential in this challenging task.

Contribution

The paper demonstrates a CNN-based system for FER that attains state-of-the-art accuracy without additional training data, through architecture tuning and optimization.

Findings

Achieved 70.10% accuracy on FER2013 dataset.

Effective hyperparameter tuning improves recognition performance.

CNN architectures can robustly recognize facial expressions.

Abstract

Over the centuries, humans have developed and acquired a number of ways to communicate. But hardly any of them can be as natural and instinctive as facial expressions. On the other hand, neural networks have taken the world by storm. And no surprises, that the area of Computer Vision and the problem of facial expressions recognitions hasn't remained untouched. Although a wide range of techniques have been applied, achieving extremely high accuracies and preparing highly robust FER systems still remains a challenge due to heterogeneous details in human faces. In this paper, we will be deep diving into implementing a system for recognition of facial expressions (FER) by leveraging neural networks, and more specifically, Convolutional Neural Networks (CNNs). We adopt the fundamental concepts of deep learning and computer vision with various architectures, fine-tune it's hyperparameters and experiment with various optimization methods and demonstrate a state-of-the-art single-network-accuracy of 70.10% on the FER2013 dataset without using any additional training data.