Comparative Evaluation of Machine Learning Algorithms for Affective State Recognition from Children's Drawings
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyEmotion ClassificationTransfer LearningSimilar Papers 제목 키워드 기반
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A Human
After the development of different machine learning and manifold learning algorithms, it may be a good time to put them together to make a powerful mind for machine. In this work, we propose affective manifolds as compon…
Dimensionality ReductionMetric LearningSupervised dimensionality reductioniMiGUE-Speech: A Spontaneous Speech Dataset for Affective Analysis
This work presents iMiGUE-Speech, an extension of the iMiGUE dataset that provides a spontaneous affective corpus for studying emotional and affective states. The new release focuses on speech and enriches the original d…
Speech Emotion RecognitionSentiment AnalysisTowards Interpretability in Audio and Visual Affective Machine Learning: A Review
Machine learning is frequently used in affective computing, but presents challenges due the opacity of state-of-the-art machine learning methods. Because of the impact affective machine learning systems may have on an in…
ArticlesDecision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Semi-Supervised Generative Adversarial Network for Stress Detection Using Partially Labeled Physiological Data
Physiological measurements involves observing variables that attribute to the normative functioning of human systems and subsystems directly or indirectly. The measurements can be used to detect affective states of a per…
AttributeGenerative Adversarial NetworkText-based Emotion Aware Recommender
We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders ba…
Collaborative FilteringRecommendation Systems