Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery
Christopher Gaul, Eduardo Fidalgo, Enrique Alegre, Roc\'io Alaiz Rodr\'iguez, Eri P\'erez Corral

TL;DR
This paper introduces a multi-task, multi-age deep learning model for accurately detecting minors in unconstrained images, addressing challenges like distribution shift and data imbalance, and demonstrating improved performance on a new comprehensive benchmark.
Contribution
The paper proposes a novel multi-task architecture with dedicated age discrimination heads, combined with data balancing techniques, and evaluates on a new large-scale benchmark for underage detection.
Findings
Reduced mean absolute error from 4.175 to 4.068 years.
Improved under-18 detection F2 score from 0.801 to 0.857.
Achieved nearly 0.99 recall on challenging domain-shifted data.
Abstract
Accurate automatic screening of minors in unconstrained images requires models robust to distribution shift and resilient to the under-representation of children in public datasets. To address these issues, we propose a multi-task architecture with dedicated under/over-age discrimination tasks based on a frozen FaRL vision-language backbone joined with a compact two-layer MLP that shares features across one age-regression head and four binary underage heads (12, 15, 18, and 21 years). This design focuses on the legally critical age range while keeping the backbone frozen. Class imbalance is mitigated through an -reweighted focal loss and age-balanced mini-batch sampling, while an age gap removes ambiguous samples near thresholds. Evaluation is conducted on our new Overall Underage Benchmark (303k cleaned training images, 110k test images), defining both the "ASORES-39k"…
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Taxonomy
TopicsFace recognition and analysis · Advanced Neural Network Applications · Domain Adaptation and Few-Shot Learning
MethodsSparse Evolutionary Training
