Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years

Abstract

Computer Vision (CV)-based human identification using orthopantomograms (OPGs) has the potential to identify unknown deceased individuals by comparing postmortem OPGs with a comprehensive antemortem CV database. However, the growing size of the CV database leads to longer processing times. This study aims to develop a standardized and reliable Convolutional Neural Network (CNN) for age estimation using OPGs and integrate it into the CV-based human identification process. The CNN was trained on 50,000 OPGs, each labeled with ages ranging from 2 to 89 years. Testing included three postmortem OPGs, 10,779 antemortem OPGs, and an additional set of 70 OPGs within the context of CV-based human identification. Integrating the CNN for age estimation into CV-based human identification process resulted in a substantial reduction of up to 96% in processing time for a CV database containing 105,251 entries. Age estimation accuracy varied between postmortem and antemortem OPGs, with a mean absolute error (MAE) of 2.76 ± 2.67 years and 3.26 ± 3.06 years across all ages, as well as 3.69 ± 3.14 years for an additional 70 OPGs. In conclusion, the incorporation of a CNN for age estimation in the CV-based human identification process significantly reduces processing time while delivering reliable results.

Artificial Intelligence in dentistry: Advances and applications in modern clinical practice

“…By training AI with pre-processed X-ray data, where they are trained to identify relevant characteristics in the images, such as structures, restorations, tooth loss and other properties, they would be able to recognize and evaluate the similarities and differences between the information collected before and after death, being able to support the expert in the decision of whether the information agrees or not (Neto et Artificial intelligence can also be used to determine age through radiographic analysis. When knowledge of the chronological age of a living or dead individual is relevant, such as in the case of victims with no proven age, the identification of juvenile offenders or the adoption of minors (Heinrich, 2024;Shen et al, 2024).…”