Age detection using clustering algorithms from children's drawings

Age detection from children's drawings is an innovative approach to understanding developmental milestones through visual analysis. Traditional methods for determining a child's age often rely on linguistic or cognitive assessments, but visual data such as drawings offer untapped potential for non-invasive analysis. This study explores the use of clustering algorithms to detect age patterns in children's drawings, providing a novel method for age estimation. A dataset of children's drawings, representing different age groups, was collected, and key visual features, such as line thickness, and object proportions, were extracted. These features were analyzed using unsupervised clustering algorithms, including K-means, Agglomerative Clustering, Mean-shift, and others, to group the drawings based on age-related visual characteristics. Among the algorithms tested, Mean-shift achieved the highest performance, with a silhouette score of 0.67 in mapping clusters to the correct age labels. K-means and Agglomerative Clustering exhibited moderate performance, with silhouette scores of 0.57 and 0.46. In contrast, Spectral Clustering and OPTICS performed poorly, with negative silhouette scores, reflecting poorly defined cluster boundaries. Our study demonstrates the potential of clustering algorithms for automatic age detection from visual features in children's drawings, despite challenges such as overlapping features between adjacent age groups. The findings suggest directions for future research, including more complex models that integrate visual and cognitive indicators for enhanced age and developmental assessment. This approach has significant implications for educational psychology, child development, and artificial intelligence.