用绘画识别儿童情绪，比较三种AI模型效果
Comparative Evaluation of Machine Learning Algorithms for Affective State Recognition from Children's Drawings
- 用迁移学习训练MobileNet、EfficientNet、VGG16三模型
- EfficientNet在准确率与速度间平衡最优
- 适合做移动端实时情绪分析的轻量级应用
自闭症谱系障碍（ASD）是一种神经发育障碍，表现为早期儿童情感表达和沟通困难。传统评估方法常具侵入性、主观性强或难以一致应用，难以早期识别儿童情绪状态。本文在前人研究基础上，对基于儿童绘画的情绪分类任务中多种机器学习模型进行对比评估。在统一实验框架下，采用迁移学习，在由心理专家标注情感标签的儿童绘画数据集上，训练并比较了MobileNet、EfficientNet和VGG16三种深度学习架构的分类性能、鲁棒性与计算效率。结果揭示了轻量级与深层架构在绘画情绪计算任务中的关键权衡，尤其适用于移动设备与实时应用场景。
原文摘要 · Abstract (English)
Autism spectrum disorder (ASD) represents a neurodevelopmental condition characterized by difficulties in expressing emotions and communication, particularly during early childhood. Understanding the affective state of children at an early age remains challenging, as conventional assessment methods are often intrusive, subjective, or difficult to apply consistently. This paper builds upon previous work on affective state recognition from children's drawings by presenting a comparative evaluation of machine learning models for emotion classification. Three deep learning architectures -- MobileNet, EfficientNet, and VGG16 -- are evaluated within a unified experimental framework to analyze classification performance, robustness, and computational efficiency. The models are trained using transfer learning on a dataset of children's drawings annotated with emotional labels provided by psychological experts. The results highlight important trade-offs between lightweight and deeper architectures when applied to drawing-based affective computing tasks, particularly in mobile and real-time application contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。