# Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations

arXiv: https://arxiv.org/abs/2201.03045 · HTML: https://mlsanity.com/paper/2201.03045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 92 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Networks with Stochastic Depth](https://mlsanity.com/paper/1603.09382/) (2016)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1512.05830/) (2015)
- [Diversity in Faces](https://mlsanity.com/paper/1901.10436/) (2019)
- [Facial Landmark Detection: a Literature Survey](https://mlsanity.com/paper/1805.05563/) (2018)
- [Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors](https://mlsanity.com/paper/1505.04868/) (2015)
- [Longitudinal Study of Child Face Recognition](https://mlsanity.com/paper/1711.03990/) (2017)
- [Large age-gap face verification by feature injection in deep networks](https://mlsanity.com/paper/1602.06149/) (2016)
- [InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity](https://mlsanity.com/paper/1712.00193/) (2017)

## Abstract (arXiv)

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.