  The precise age estimation of child sexual abuse and exploitation (CSAE)
victims is one of the most significant digital forensic challenges.
Investigators often need to determine the age of victims by looking at images
and interpreting the sexual development stages and other human characteristics.
The main priority - safeguarding children -- is often negatively impacted by a
huge forensic backlog, cognitive bias and the immense psychological stress that
this work can entail. This paper evaluates existing facial image datasets and
proposes a new dataset tailored to the needs of similar digital forensic
research contributions. This small, diverse dataset of 0 to 20-year-old
individuals contains 245 images and is merged with 82 unique images from the
FG-NET dataset, thus achieving a total of 327 images with high image diversity
and low age range density. The new dataset is tested on the Deep EXpectation
(DEX) algorithm pre-trained on the IMDB-WIKI dataset. The overall results for
young adolescents aged 10 to 15 and older adolescents/adults aged 16 to 20 are
very encouraging -- achieving MAEs as low as 1.79, but also suggest that the
accuracy for children aged 0 to 10 needs further work. In order to determine
the efficacy of the prototype, valuable input of four digital forensic experts,
including two forensic investigators, has been taken into account to improve
age estimation results. Further research is required to extend datasets both
concerning image density and the equal distribution of factors such as gender
and racial diversity.
