Smartphone-based biological sex identification from palatal rugae images: a comparative transfer learning study of pretrained convolutional neural networks

Abstract Background Palatal rugae are distinct anatomical ridges on the anterior part of the palate that exhibit individual-specific morphological patterns, making them valuable markers for personal identification and forensic analysis. With advancements in artificial intelligence (AI), deep learning methods have emerged as effective tools for extracting discriminative features from medical and biometric images. This study investigates the potential of transfer learning-based convolutional neural networks (CNNs) for biological sex classification using palatal rugae images captured through a smartphone camera. Results All pretrained models successfully learned discriminative biological sex -related features from palatal rugae images. Among them, Residual network 50 (ResNet50) and Visual Geometry Group 16 (VGG16) achieved the highest classification accuracy with optimal precision and recall, while EfficientNet-B0 and Dense Network 121 (DenseNet) demonstrated comparable but slightly lower performance. The Receiver Operating Curve (ROC curve) exhibited high Area under the Curve (AUC) values, confirming the strong separability between male and female classes. Conclusion The findings confirm that smartphone-based imaging combined with transfer learning offers a promising, low-cost, and non-invasive approach for biological sex identification using palatal rugae patterns, as an adjunct tool for forensic odontology. Among the evaluated models, ResNet50 showed the most robust performance. Future work will focus on expanding the dataset and incorporating explainable Artificial Intelligence methods to support clinical and forensic applicability.