Publications

@article{chaves_data_2023,

title = {A Data Augmentation Strategy for Improving Age Estimation to Support CSEM Detection},

author = {D. Chaves and N. Agarwal and Eduardo Fidalgo and Enrique Alegre},

url = {https://www.scitepress.org/PublishedPapers/2023/117197/117197.pdf},

year  = {2023},

date = {2023-01-01},

journal = {Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},

volume = {5},

number = {ISBN 978-989-758-634-7, ISSN 2184-4321},

pages = {692–699},

abstract = {Leveraging image-based age estimation in preventing Child Sexual Exploitation Material (CSEM) content over the internet is not investigated thoroughly in the research community. While deep learning methods are considered state-of-the-art for general age estimation, they perform poorly in predicting the age group of minors and older adults due to the few examples of these age groups in the existing datasets. In this work, we present a data augmentation strategy to improve the performance of age estimators trained on imbalanced data based on synthetic image generation and artificial facial occlusion. Facial occlusion is focused on modelling as CSEM criminals tend to cover certain parts of the victim, such as the eyes, to hide their identity. The proposed strategy is evaluated using the Soft Stagewise Regression Network (SSR-Net), a compact size age estimator and three publicly available datasets composed mainly of non-occluded images. Therefore, we create the Synthetic Augmented with Occluded Faces (SAOF-15K) dataset to assess the performance of eye and mouthoccluded images. Results show that our strategy improves the performance of the evaluated age estimator.},

note = {Publisher: 10.5220/0011719700003417},

keywords = {age stimation, CSEM, data augmentation, facial occlusion, prevention, synthetic datasets},

pubstate = {published},

tppubtype = {article}

}