Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery

Abstract: Accurate automatic screening of minors in unconstrained images demands models that are robust to distribution shift and resilient to the children under-representation in publicly available data. To overcome 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 under-age heads for age thresholds of 12, 15, 18, and 21 years, focusing on the legally critical age range. To address the severe class imbalance, we introduce an $\alpha$-reweighted focal-style loss and age-balanced mini-batch sampling, which equalizes twelve age bins during stochastic optimization. Further improvement is achieved with an age gap that removes edge cases from the loss. Moreover, we set a rigorous evaluation by proposing the Overall Under-Age Benchmark, with 303k cleaned training images and 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 pose ($>$45{\deg}), expression, and low image quality to emulate real-world shifts. Trained on the cleaned overall set with resampling and age gap, our multiage model "F" lowers the root-mean-square-error on the ASORES-39k restricted test from 5.733 (age-only baseline) to 5.656 years and lifts under-18 detection from F2 score of 0.801 to 0.857 at 1% false-adult rate. Under the domain shift to the wild data of ASWIFT-20k, the same configuration nearly sustains 0.99 recall while boosting F2 from 0.742 to 0.833 with respect to the age-only baseline, demonstrating strong generalization under distribution shift. For the under-12 and under-15 tasks, the respective boosts in F2 are from 0.666 to 0.955 and from 0.689 to 0.916, respectively.