Background/Objectives: Anthropometric measurements provide essential normative datasets that form the foundation for clinical practice and forensic identification. The human ear is a highly informative structure due to its complex morphology and individual specificity, making it a valuable tool for biometric systems. This study aimed to estimate biological sex based on auricular morphometric measurements, develop a logistic regression model for this purpose, and validate its performance using ROC analysis. Materials and Methods: This cross-sectional study included 120 adult participants (60 males, 60 females). Standardized digital photographs were analyzed in ImageJ to record 22 linear and 6 angular measurements using established anatomical landmarks. LASSO logistic regression was employed for variable selection and model shrinkage. The final model’s discriminative performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer–Lemeshow test, and the Brier score. Results: A comparative analysis revealed that most linear and angular measurements showed significant sexual dimorphism. Almost all linear dimensions (A1–A22) were significantly larger in males (p < 0.001). Auricular width (A2) and width at the level of the tragus (A3) emerged as the most robust indicators, demonstrating “very large” effect sizes. Conversely, the angle between the preauricular line and the vertical plane (A28) was significantly greater in females, providing a unique inverse relationship for sex estimation. A parsimonious 5-predictor model (incorporating A2, A3, A5, A10, and A28) achieved exceptional discriminative performance with an AUC of 0.980. Conclusions: Auricular morphometry is a highly effective tool for sex estimation. The findings confirm significant sexual dimorphism in the external ear, particularly in linear dimensions. The developed model may serve as a preliminary morphometric reference for future automated biometric recognition studies, although no artificial intelligence-based classification model was developed in the present study.
Babacan et al. (Fri,) studied this question.