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.

翻译：自闭症谱系障碍（ASD）是一种神经发育障碍，其特征表现为情感表达与沟通困难，在幼儿期尤为明显。由于传统评估方法通常具有侵入性、主观性或难以保持一致性，早期识别儿童情感状态仍具挑战性。本文在先前儿童绘画情感状态识别研究的基础上，对情感分类的机器学习模型进行了比较评估。研究在统一的实验框架内评估了三种深度学习架构——MobileNet、EfficientNet 和 VGG16——以分析其分类性能、鲁棒性和计算效率。这些模型采用迁移学习方法，在由心理学专家标注情感标签的儿童绘画数据集上进行训练。研究结果揭示了轻量级架构与深层架构在应用于基于绘画的情感计算任务时的重要权衡，特别是在移动与实时应用场景中。