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}

}

@article{jeuland_assessment_2022,

title = {Assessment of age estimation methods for forensic applications using non-occluded and synthetic occluded facial images},

author = {Elouan Derenee Jeuland and Aitor Del Río Ferreras and Deisy Chaves and Eduardo Fidalgo and Víctor González Castro and Enrique Alegre},

url = {https://ruc.udc.es/dspace/handle/2183/31412},

year  = {2022},

date = {2022-01-01},

journal = {XLIII Jornadas de Automática},

pages = {972–979},

abstract = {This paper evaluates the performance of six deep-learning-based age estimators for forensic applications, particularly in identifying minors and offenders in Child Sexual Exploitation Materials (CSEM). While deep learning is the state-of-the-art for age estimation, it struggles with minors and older adults due to dataset imbalances. Additionally, offenders often use facial occlusion to obscure identities, further impacting estimator accuracy. The study assesses models on non-occluded and synthetically occluded datasets, revealing that eye occlusion has a greater effect than mouth occlusion. Minors and elderly individuals are the most affected by occlusion, making this research a valuable benchmark for forensic victim profiling.},

note = {Publisher: Universidade da Coruña. Servizo de Publicacións},

keywords = {Age Estimation, CSEM, deep learning, facial occlusion},

pubstate = {published},

tppubtype = {article}

}