# Improving Facial Emotion Recognition through Dataset Merging and Balanced Training Strategies

arXiv: https://arxiv.org/abs/2604.20307 · HTML: https://mlsanity.com/paper/2604.20307/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution](https://mlsanity.com/paper/1608.01041/) (2016)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
- [HTNet for micro-expression recognition](https://mlsanity.com/paper/2307.14637/) (2023)
- [Joint Face Detection and Facial Motion Retargeting for Multiple Faces](https://mlsanity.com/paper/1902.10744/) (2019)
- [Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey](https://mlsanity.com/paper/2107.05989/) (2021)
- [Weighted Random Sampling over Joins](https://mlsanity.com/paper/2201.02670/) (2022)

## Built on by (most cited first, 1 of 1)

- [Improving Driver Drowsiness Detection via Personalized EAR/MAR Thresholds and CNN-Based Classification](https://mlsanity.com/paper/2604.22479/) (2026)

## Abstract (arXiv)

In this paper, a deep learning framework is proposed for automatic facial emotion based on deep convolutional networks. In order to increase the generalization ability and the robustness of the method, the dataset size is increased by merging three publicly available facial emotion datasets: CK+, FER+ and KDEF. Despite the increase in dataset size, the minority classes still suffer from insufficient number of training samples, leading to data imbalance. The data imbalance problem is minimized by online and offline augmentation techniques and random weighted sampling. Experimental results demonstrate that the proposed method can recognize the seven basic emotions with 82% accuracy. The results demonstrate the effectiveness of the proposed approach in tackling the challenges of data imbalance and improving classification performance in facial emotion recognition.