Abstract
Facial Emotion Recognition (FER) is a key task in affective computing, enabling applications in human-computer interaction, e-learning, healthcare, and safety systems. Despite advances in deep learning, FER remains challenging due to occlusions, illumination and pose variations, subtle intra-class differences, and dataset imbalance that hinders recognition of minority emotions. We present InsideOut, a reproducible FER framework built on EfficientNetV2-S with transfer learning, strong data augmentation, and imbalance-aware optimization. The approach standardizes FER2013 images, applies stratified splitting and augmentation, and fine-tunes a lightweight classification head with class-weighted loss to address skewed distributions. InsideOut achieves 62.8% accuracy with a macro averaged F1 of 0.590 on FER2013, showing competitive results compared to conventional CNN baselines. The novelty lies in demonstrating that efficient architectures, combined with tailored imbalance handling, can provide practical, transparent, and reproducible FER solutions.
Abstract (translated)
面部情绪识别（FER）是情感计算中的一个关键任务，它能够支持人机交互、电子学习、医疗保健和安全系统等应用。尽管深度学习技术取得了进展，但由于遮挡、光照变化和姿势变化等因素的影响，以及细微的类内差异和数据集不平衡问题的存在，使得FER仍然具有挑战性，这些问题阻碍了少数情绪的识别。 我们提出了InsideOut框架，这是一个基于EfficientNetV2-S架构并通过迁移学习、强大的数据增强技术和针对不平衡处理优化方法构建的情绪识别可重复研究框架。该方法对FER2013数据集中的图像进行标准化处理，并采用了分层拆分和增强技术，同时通过对轻量级分类头采用类别加权损失进行微调来解决偏斜分布的问题。 InsideOut在FER2013数据集上实现了62.8%的准确率以及0.590的宏平均F1分数，与传统的卷积神经网络基准相比表现出了竞争力。其创新之处在于展示了高效架构结合专门处理不平衡的方法可以提供实用、透明且可重复的情绪识别解决方案。 该框架和方法对于在资源受限环境下提高FER性能具有重要意义，并为未来的研究提供了坚实的基础。
URL
https://arxiv.org/abs/2510.03066
