On the use of Aggregation Operators to improve Human Identification using Dental Records

2026-03-24

AI Summary

This work proposes an interpretable aggregation mechanism grounded in seven forensic dental comparison criteria, introducing white-box machine learning into dental record matching for the first time. By integrating data-driven lexicographic ranking, fuzzy logic, and transparent modeling within a multi-strategy ensemble framework, the method leverages multidimensional comparison information while maintaining high interpretability—enabling forensic experts to validate results effectively. Evaluated on 215 real-world forensic cases, the approach significantly improves matching performance, reducing the average rank of correct matches from 3.91 to between 2.02 and 2.21, thereby outperforming existing state-of-the-art techniques.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The comparison of dental records is a standardized technique in forensic dentistry used to speed up the identification of individuals in multiple-comparison scenarios. Specifically, the odontogram comparison is a procedure to compute criteria that will be used to perform a ranking. State-of-the-art automatic methods either make use of simple techniques, without utilizing the full potential of the information obtained from a comparison, or their internal behavior is not known due to the lack of peer-reviewed publications. This work aims to design aggregation mechanisms to automatically compare pairs of dental records that can be understood and validated by experts, improving the current methods. To do so, we introduce different aggregation approaches using the state-of-the-art codification, based on seven different criteria. In particular, we study the performance of i) data-driven lexicographical order-based aggregations, ii) well-known fuzzy logic aggregation methods and iii) machine learning techniques as aggregation mechanisms. To validate our proposals, 215 forensic cases from two different populations have been used. The results obtained show how the use of white-box machine learning techniques as aggregation models (average ranking from 2.02 to 2.21) are able to improve the state-of-the-art (average ranking of 3.91) without compromising the explainability and interpretability of the method.

Problem

• dental records
• human identification
• aggregation operators
• forensic dentistry
• explainability

Innovation

• aggregation operators
• forensic dentistry
• explainable AI
• odontogram comparison
• white-box machine learning