DeepUAge: Improving Underage Age Estimation Accuracy to Aid CSEM Investigation

Age is a soft biometric trait that can aid law enforcement in the identification of victims of Child Sexual Exploitation Material (CSEM) creation/distribution. Accurate age estimation of subjects can classify explicit content possession as illegal during an investigation. Automation of this age classification has the potential to expedite content discovery and focus the investigation of digital evidence through the prioritisation of evidence containing CSEM. In recent years, artificial intelligence based approaches for automated age estimation have been created, and many public cloud service providers offer this service on their platforms. The accuracy of these algorithms have been improving over recent years. These existing approaches perform satisfactorily for adult subjects, but perform wholly inadequately for underage subjects. To this end, the largest underage facial age dataset, VisAGe, has been used in this work to train a ResNet50 based deep learning model, DeepUAge, that achieved state-of-the-art beating performance for age estimation of minors. This paper describes the design and implementation of this model. An evaluation, validation and comparison of the proposed model is performed against existing facial age classifiers resulting in the best overall performance for underage subjects.