Comparison of deep learning classification models for facial image age estimation in digital forensic investigations

There has been a significant rise in digital forensic investigations containing Indecent Images of Children (IIoC), and one of the major challenges faced by investigators is the time-consuming task of manually investigating images for illicit content. In the UK, law enforcement maintains and uses a standard national repository of IIoC, known as CAID (Child Abuse Image Database), to identify known illegal images by matching their image hashes and metadata. The CAID plays a significant role in making IIoC investigations faster and more effective. However, all images that are not matched through using CAID require manual analysis. Every image has to be viewed and verified as IIoC by investigators. The victim age estimation in the images (i.e., determining whether they are juvenile or adult as this would change the course of the investigation) is a crucial part of this verification process and takes time due to a large number of images to inspect, therefore impacting the speed of the investigation, and consequently victims. This is a time-consuming and challenging task for human investigators. Previous work has demonstrated that deep learning has the capability to estimate age with high accuracy in images. This reduces the number of images that will need to be manually processed, thereby finishing the investigation faster. However, in terms of practical implementation in IIoC investigations, there is an absence of a comparative study using the same datasets to establish the most appropriate deep learning model and classification approach to use. This is important as different models have different capabilities and previous works utilise various binary, multi-class, and regression approaches. It is not yet known which is the most accurate for use in digital forensic investigations. In this paper, we construct an extensive dataset before experimenting with four pre-trained deep learning models: VGG16, ResNet50, Xception, and InceptionV3. We have identified that binary classification works best for the identification of images as a child or adult, with the ResNet50 obtaining the best results in terms of accuracy (91.70%) on unseen images.