针对儿童表情的喜怒分类，构建了高精度识别模型。
Emotion Classification of Children Expressions
- 引入注意力机制与数据增强，提升儿童表情识别能力
- 模型准确率达89%，优于传统成人导向系统
- 适合教育科技、心理健康辅助等场景使用
本文提出一种针对儿童面部表情的分类流程，旨在区分'开心'与'悲伤'两类情绪。由于现有情绪识别系统多基于成人面部训练，本研究采用含Squeeze-and-Excitation模块、卷积块注意力模块的先进模型结构，并结合Stable Diffusion进行图像合成，扩展并多样化数据集，生成逼真多样的训练样本。通过引入批归一化、丢弃层及SE注意力机制，所设计模型在儿童情绪分类任务上达到89%的准确率，显著提升了对儿童情绪识别的精度。研究强调为青少年群体开发专用情绪检测模型的重要性，指出该技术可助力在线环境下儿童情绪管理与心理支持。
原文摘要 · Abstract (English)
This paper proposes a process for a classification model for the facial expressions. The proposed process would aid in specific categorisation of children's emotions from 2 emotions namely 'Happy' and 'Sad'. Since the existing emotion recognition systems algorithms primarily train on adult faces, the model developed is achieved by using advanced concepts of models with Squeeze-andExcitation blocks, Convolutional Block Attention modules, and robust data augmentation. Stable Diffusion image synthesis was used for expanding and diversifying the data set generating realistic and various training samples. The model designed using Batch Normalisation, Dropout, and SE Attention mechanisms for the classification of children's emotions achieved an accuracy rate of 89\% due to these methods improving the precision of emotion recognition in children. The relative importance of this issue is raised in this study with an emphasis on the call for a more specific model in emotion detection systems for the young generation with specific direction on how the young people can be assisted to manage emotions while online.
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