Improving Age Estimation In Minors And Young Adults With Occluded Faces To Fight Against Child Sexual Exploitation

Accurate and fast age estimation is crucial in systems for detecting possible victims in Child Sexual Exploitation Materials. Age estimation obtains state of the art results with deep learning. However, these models tend to perform poorly in minors and young adults, because they are trained with unbalanced data and few examples. Furthermore, some Child Sexual Exploitation images present eye occlusion to hide the identity of the victims, which may also affect the performance of age estimators. In this work, we evaluate the performance of Soft Stagewise Regression Network (SSR-Net), a compact size age estimator model, with non-occluded and occluded face images. We propose an approach to improve the age estimation in minors and young adults by using both types of facial images to create SSR-Net models. The proposed strategy builds robust age estimators that improve SSR-Net pre-trained models on IMBD and MORPH datasets, and a Deep EXpectation model, reducing the Mean Absolute Error (MAE) from 7.26, 6.81 and 6.5 respectively, to 4.07 with our proposal.