Compartir
Título
A data augmentation strategy for improving age estimation to support CSEM detection
Autor
Facultad/Centro
Área de conocimiento
Asignaturas
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Datos de la obra
Chaves, D.; Agarwal, N.; Fidalgo, E. and Alegre, E. (2023). A data augmentation strategy for improving age estimation to support CSEM detection. En Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5. 692-699. DOI: 10.5220/0011719700003417
Editor
SciTePress
Fecha
2023
Zusammenfassung
[EN] 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.
Materia
Palabras clave
URI
Aparece en las colecciones
Dateien zu dieser Ressource
Tamaño:
1.195
xmlui.dri2xhtml.METS-1.0.size-megabytes
Formato:
Adobe PDF