# Facial Emotion Recognition using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1910.05602 · HTML: https://mlsanity.com/paper/1910.05602/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space](https://mlsanity.com/paper/2405.01828/) (2024)
- [Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction](https://mlsanity.com/paper/2206.09509/) (2022)
- [Facial Emotion Recognition: A multi-task approach using deep learning](https://mlsanity.com/paper/2110.15028/) (2021)
- [Learning to Hear by Seeing: It's Time for Vision Language Models to Understand Artistic Emotion from Sight and Sound](https://mlsanity.com/paper/2511.12077/) (2025)
- [Real-time Emotion and Gender Classification using Ensemble CNN](https://mlsanity.com/paper/2111.07746/) (2021)
- [Facial Emotion Recognition on FER-2013 using an EfficientNetB2-Based Approach](https://mlsanity.com/paper/2601.18228/) (2026)

## Abstract (arXiv)

Facial expression recognition is a topic of great interest in most fields from artificial intelligence and gaming to marketing and healthcare. The goal of this paper is to classify images of human faces into one of seven basic emotions. A number of different models were experimented with, including decision trees and neural networks before arriving at a final Convolutional Neural Network (CNN) model. CNNs work better for image recognition tasks since they are able to capture spacial features of the inputs due to their large number of filters. The proposed model consists of six convolutional layers, two max pooling layers and two fully connected layers. Upon tuning of the various hyperparameters, this model achieved a final accuracy of 0.60.