JAM: A Comprehensive Model for Age Estimation, Verification, and Comparability

2024-10-05

arXiv.org

AI Summary

Existing age estimation methods suffer from low accuracy, poor robustness, and task fragmentation—particularly in security and identity verification applications. To address these limitations, this paper proposes the first unified framework jointly modeling age estimation, age verification, and cross-sample age comparability judgment. Methodologically, we design a deep representation learning–based multi-task architecture, introduce confidence-driven probabilistic age interval prediction, and integrate distribution-aware loss with uncertainty calibration to significantly enhance robustness and reliability on ambiguous samples. Experimental results demonstrate state-of-the-art performance: our model ranks among the top performers in multiple tracks of the NIST FATE Challenge and consistently outperforms existing SOTA methods across diverse public and private benchmarks. The framework establishes a new paradigm for age-related intelligent decision-making—achieving superior accuracy, interpretability, and generalizability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements

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.

Problem

• Age Judgement
• Comprehensive Model
• Accuracy Improvement

Innovation

• JAM Model
• Advanced Learning Algorithms
• Age Identification