Botox Detection and Face Analytics Using Deep Learning

Abstract

Non-surgical cosmetic procedures like Botox are increasingly common, yet their impact on facial analytics systems remains unexplored. We curate a novel dataset of 1,990 before-and-after images from 390 individuals who received cosmetic injectables. Using this dataset, we demonstrate that these subtle facial modifications measurably affect age estimation: FairFace and FaceXFormer show statistically significant shifts toward younger age estimates (-1.43 and -3.27 years, p <0.05), while MiVOLO remains stable. We also show these modifications are detectable: training deep learning models (ResNet-50, DenseNet-121, ConvNeXtTiny) to classify cosmetically-altered faces achieves up to 89% accuracy. Our findings reveal that even minor, non-surgical facial changes can bias age-based analytics and are algorithmically detectable-raising critical concerns for privacy, fairness, and robustness as facial analytics expand into high-stakes domains like insurance, hiring, and health assessment.