🤖 AI Summary
This study addresses the uncertainty arising from missing ground truth and image degradation in sex determination of prehistoric hand stencils by proposing an end-to-end uncertainty-aware deep learning framework. By integrating ensemble learning, contour enhancement, and manifold mapping, the method explicitly models and propagates uncertainty throughout the analytical pipeline while employing explainable AI to verify anatomical consistency, thereby transforming uncertainty into a quantifiable component of archaeological inference. Achieving over 88% accuracy on modern samples, the framework effectively distinguishes between morphologically stable and ambiguous cases. Consequently, this work enables robust decoding, confidence quantification, and reproducible inference regarding sex attribution in prehistoric rock art, offering a rigorous computational approach to mitigating epistemic limitations in paleoanthropological research.
📝 Abstract
Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of ground truth, population differences between contemporary and prehistoric groups, and the uncertainty introduced by image degradation. Traditional morphometric methods suffer from high structural overlap across sexes, poor cross-population generalizability, and subjective feature engineering. This study presents an uncertainty-aware deep learning framework for sex attribution in prehistoric hand stencils that explicitly models, propagates, and aggregates uncertainty throughout the analytical pipeline. The methodology combines dual image processing, dual contour extraction, structured silhouette augmentation, model architectural diversity, and ensemble-based decision aggregation. The pipeline generates twelve plausible silhouette realizations per stencil to capture boundary uncertainties, which are processed by two ensembles of ten deep neural networks each (EfficientNet-B3 and MobileViT-S) trained on 14,036 contemporary hand samples. Furthermore, a triangulated validation scheme integrates ensemble predictions with unsupervised 2D latent-space manifold mapping (UMAP + k-NN) and explainable AI spatial attributions (LayerCAM) to ensure anatomical consistency. On contemporary data, ensemble models achieve strong classification performance, with accuracies exceeding 88% in older age groups. When applied to prehistoric stencils, the framework produces both sex predictions and confidence measures of internal agreement, enabling the distinction between morphologically stable and ambiguous cases. Convergence across ensemble predictions, latent-space structure, and interpretability analyses shows that uncertainty can become a measurable component of archaeological inference, enabling robust and reproducible decoding of ancient rock art.