OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation

2025-05-08

AI Summary

This study addresses the challenges of subtle physiological changes and scarce longitudinal data in pediatric age estimation by proposing a multi-task deep learning framework leveraging near-infrared iris and periocular images. We introduce the first 8-year longitudinal pediatric ocular biometric benchmark—comprising over 21,000 images—and design a lightweight CNN architecture supporting non-square inputs, jointly optimizing age regression and age-group classification. Experiments demonstrate that the periocular region substantially outperforms the iris, achieving a mean absolute error (MAE) of 1.33 years and age-group classification accuracy of 83.82%. The model exhibits strong cross-device generalization and enables real-time inference on VR platforms (<10 ms). Key contributions include: (1) the first longitudinal pediatric ocular biometric benchmark; (2) empirical validation of periocular dominance for age estimation; and (3) an efficient, edge-deployable multi-task framework tailored for resource-constrained environments.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Life-Long and Continual Learning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Estimating a child's age from ocular biometric images is challenging due to subtle physiological changes and the limited availability of longitudinal datasets. Although most biometric age estimation studies have focused on facial features and adult subjects, pediatric-specific analysis, particularly of the iris and periocular regions, remains relatively unexplored. This study presents a comparative evaluation of iris and periocular images for estimating the ages of children aged between 4 and 16 years. We utilized a longitudinal dataset comprising more than 21,000 near-infrared (NIR) images, collected from 288 pediatric subjects over eight years using two different imaging sensors. A multi-task deep learning framework was employed to jointly perform age prediction and age-group classification, enabling a systematic exploration of how different convolutional neural network (CNN) architectures, particularly those adapted for non-square ocular inputs, capture the complex variability inherent in pediatric eye images. The results show that periocular models consistently outperform iris-based models, achieving a mean absolute error (MAE) of 1.33 years and an age-group classification accuracy of 83.82%. These results mark the first demonstration that reliable age estimation is feasible from children's ocular images, enabling privacy-preserving age checks in child-centric applications. This work establishes the first longitudinal benchmark for pediatric ocular age estimation, providing a foundation for designing robust, child-focused biometric systems. The developed models proved resilient across different imaging sensors, confirming their potential for real-world deployment. They also achieved inference speeds of less than 10 milliseconds per image on resource-constrained VR headsets, demonstrating their suitability for real-time applications.

Problem

• Estimating pediatric age from iris and periocular images
• Comparing iris vs periocular models for age prediction
• Developing child-focused biometric systems using deep learning

Innovation

• Multi-task deep learning for pediatric age estimation
• Longitudinal NIR dataset with 21,000+ images
• CNN adapted for non-square ocular inputs