# Facial Emotion Recognition: State of the Art Performance on FER2013

arXiv: https://arxiv.org/abs/2105.03588 · HTML: https://mlsanity.com/paper/2105.03588/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 8 May 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [A Sufficient Condition for Convergences of Adam and RMSProp](https://mlsanity.com/paper/1811.09358/) (2018)
- [An Exponential Learning Rate Schedule for Deep Learning](https://mlsanity.com/paper/1910.07454/) (2019)
- [A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation](https://mlsanity.com/paper/1810.13243/) (2018)
- [Optimization for deep learning: theory and algorithms](https://mlsanity.com/paper/1912.08957/) (2019)
- [Going Deeper in Facial Expression Recognition using Deep Neural Networks](https://mlsanity.com/paper/1511.04110/) (2015)
- [Deep Learning using Linear Support Vector Machines](https://mlsanity.com/paper/1306.0239/) (2013)
- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
- [Local Learning with Deep and Handcrafted Features for Facial Expression Recognition](https://mlsanity.com/paper/1804.10892/) (2018)
- [Learning to Amend Facial Expression Representation via De-albino and Affinity](https://mlsanity.com/paper/2103.10189/) (2021)
- [Facial Expression Recognition using Convolutional Neural Networks: State of the Art](https://mlsanity.com/paper/1612.02903/) (2016)
- [Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks](https://mlsanity.com/paper/1302.1700/) (2013)

## Built on by (most cited first, 20 of 20)

- [Revisiting Weight Averaging for Model Merging](https://mlsanity.com/paper/2412.12153/) (2024)
- [ModernVBERT: Towards Smaller Visual Document Retrievers](https://mlsanity.com/paper/2510.01149/) (2025)
- [Local Multi-Head Channel Self-Attention for Facial Expression Recognition](https://mlsanity.com/paper/2111.07224/) (2021)
- [EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition](https://mlsanity.com/paper/2501.08199/) (2025)
- [A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation](https://mlsanity.com/paper/2201.12705/) (2022)
- [Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People](https://mlsanity.com/paper/2406.04278/) (2024)
- [Product Re-identification System in Fully Automated Defect Detection](https://mlsanity.com/paper/2112.10324/) (2021)
- [Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network](https://mlsanity.com/paper/2201.09061/) (2022)
- [Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition](https://mlsanity.com/paper/2502.00547/) (2025)
- [FExGAN-Meta: Facial Expression Generation with Meta Humans](https://mlsanity.com/paper/2203.05975/) (2022)
- [Adaptive Discriminative Regularization for Visual Classification](https://mlsanity.com/paper/2203.00833/) (2022)
- [The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning](https://mlsanity.com/paper/2304.09914/) (2023)
- [Facial Emotion Recognition in VR Games](https://mlsanity.com/paper/2312.06925/) (2023)
- [Target-Dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-Caption Translation Network using Visual-Caption Pairs](https://mlsanity.com/paper/2408.10248/) (2024)
- [Emotion Recognition with Facial Attention and Objective Activation Functions](https://mlsanity.com/paper/2410.17740/) (2024)
- [Emotion estimation from video footage with LSTM](https://mlsanity.com/paper/2501.13432/) (2025)
- [Achieving 3D Attention via Triplet Squeeze and Excitation Block](https://mlsanity.com/paper/2505.05943/) (2025)
- [Evaluating Open-Source Vision Language Models for Facial Emotion Recognition against Traditional Deep Learning Models](https://mlsanity.com/paper/2508.13524/) (2025)
- [Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection](https://mlsanity.com/paper/2605.30099/) (2026)
- [Does Model Uncertainty Track Human Ambiguity? Evidence from Multi-Annotator Vision Benchmarks](https://mlsanity.com/paper/2609.34506/) (2026)

## Abstract (arXiv)

Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techniques for FER, deep learning models, especially Convolutional Neural Networks (CNNs) have shown great potential due to their powerful automatic feature extraction and computational efficiency. In this work, we achieve the highest single-network classification accuracy on the FER2013 dataset. We adopt the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods. To our best knowledge, our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.