JAM: A Comprehensive Model for Age Estimation, Verification, and Comparability
Fran\c{c}ois David, Alexey A. Novikov, Ruslan Parkhomenko, Artem, Voronin, Alix Melchy

TL;DR
This paper presents a comprehensive age estimation model that employs advanced learning techniques and probabilistic methods, demonstrating top performance in various datasets and NIST evaluations for diverse applications.
Contribution
It introduces a novel integrated model for age estimation, verification, and comparability, with probabilistic age ranges and high accuracy across multiple datasets.
Findings
Achieved top rankings in NIST FATE challenge
Outperformed existing models on proprietary and public datasets
Effectively handles ambiguous age cases using confidence scores
Abstract
This paper introduces a comprehensive model for age estimation, verification, and comparability, offering a comprehensive solution for a wide range of applications. It employs advanced learning techniques to understand age distribution and uses confidence scores to create probabilistic age ranges, enhancing its ability to handle ambiguous cases. The model has been tested on both proprietary and public datasets and compared against one of the top-performing models in the field. Additionally, it has recently been evaluated by NIST as part of the FATE challenge, achieving top places in many categories.
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Taxonomy
TopicsInsurance, Mortality, Demography, Risk Management
