# Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery Source: [Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery]() Collection index: [llms.txt]() Data updated: 2026-05-27T05:00:21.000Z Authors: Christopher Gaul, Eduardo Fidalgo, Enrique Alegre, Roc\\'io Alaiz Rodr\\'iguez, Eri P\\'erez Corral Published: 2025-06-12T13:36:27.000Z ## 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 $\\alpha$-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" restricted overall test, which removes the noisiest domains, and the age estimation wild-shifts test "ASWIFT-20k" of 20k-images, stressing extreme poses ($\>$45{\\deg}), expressions, and low image quality to emulate real-world shifts. Trained on the cleaned overall set with resampling and age gap, our multiage model "F" reduces the mean absolute error on ASORES-39k from 4.175 y (age-only baseline) to 4.068 y and improves under-18 detection from F2 score of 0.801 to 0.857 at 1% false-adult rate. Under the ASWIFT-20k, the same configuration nearly sustains 0.99 recall while F2 rises from 0.742 to 0.833, demonstrating robustness to domain shift. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Age Classification on occluded + original (test)](): Age Classification; occluded + original (test); Accuracy: 47.6. - [Age Estimation on occluded + original (test)](): Age Estimation; occluded + original (test); MAE: 4.11. ## Original sources and implementation [Original paper]() [PDF]()