# ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition

arXiv: https://arxiv.org/abs/2409.10545 · HTML: https://mlsanity.com/paper/2409.10545/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 38 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018)
- [Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond](https://mlsanity.com/paper/2401.01219/) (2024)
- [Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition](https://mlsanity.com/paper/2109.07270/) (2021)
- [From Facial Expression Recognition to Interpersonal Relation Prediction](https://mlsanity.com/paper/1609.06426/) (2016)
- [POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition](https://mlsanity.com/paper/2204.04083/) (2022)
- [From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2312.05447/) (2023)
- [Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition](https://mlsanity.com/paper/2212.05463/) (2022)
- [Local Learning with Deep and Handcrafted Features for Facial Expression Recognition](https://mlsanity.com/paper/1804.10892/) (2018)
- [HTNet for micro-expression recognition](https://mlsanity.com/paper/2307.14637/) (2023)
- [Learning to Amend Facial Expression Representation via De-albino and Affinity](https://mlsanity.com/paper/2103.10189/) (2021)
- [SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis](https://mlsanity.com/paper/1808.02531/) (2018)
- [ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning](https://mlsanity.com/paper/2305.01486/) (2023)
- [Local Multi-Head Channel Self-Attention for Facial Expression Recognition](https://mlsanity.com/paper/2111.07224/) (2021)
- [LRDif: Diffusion Models for Under-Display Camera Emotion Recognition](https://mlsanity.com/paper/2402.00250/) (2024)
- [Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition](https://mlsanity.com/paper/2401.11085/) (2024)

## Built on by (most cited first, 9 of 9)

- [FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images](https://mlsanity.com/paper/2505.07530/) (2025)
- [Uncertainty-Aware Multimodal Emotion Recognition through Dirichlet Parameterization](https://mlsanity.com/paper/2602.09121/) (2026)
- [Face-Guided Sentiment Boundary Enhancement for Weakly-Supervised Temporal Sentiment Localization](https://mlsanity.com/paper/2603.14750/) (2026)
- [Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model](https://mlsanity.com/paper/2411.10863/) (2024)
- [Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design](https://mlsanity.com/paper/2501.18538/) (2025)
- [An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images](https://mlsanity.com/paper/2504.21309/) (2025)
- [A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm](https://mlsanity.com/paper/2508.17969/) (2025)
- [ExpressNet-MoE: A Hybrid Deep Neural Network for Emotion Recognition](https://mlsanity.com/paper/2510.13493/) (2025)
- [PriorProbe: Recovering Individual-Level Priors for Personalizing Neural Networks in Facial Expression Recognition](https://mlsanity.com/paper/2602.03882/) (2026)

## Abstract (arXiv)

The human face is a silent communicator, expressing emotions and thoughts through its facial expressions. With the advancements in computer vision in recent years, facial emotion recognition technology has made significant strides, enabling machines to decode the intricacies of facial cues. In this work, we propose ResEmoteNet, a novel deep learning architecture for facial emotion recognition designed with the combination of Convolutional, Squeeze-Excitation (SE) and Residual Networks. The inclusion of SE block selectively focuses on the important features of the human face, enhances the feature representation and suppresses the less relevant ones. This helps in reducing the loss and enhancing the overall model performance. We also integrate the SE block with three residual blocks that help in learning more complex representation of the data through deeper layers. We evaluated ResEmoteNet on four open-source databases: FER2013, RAF-DB, AffectNet-7 and ExpW, achieving accuracies of 79.79%, 94.76%, 72.39% and 75.67% respectively. The proposed network outperforms state-of-the-art models across all four databases. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet.