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Título
Assessment of age estimation methods for forensic applications using non-occluded and synthetic occluded facial images
Autor
Facultad/Centro
Área de conocimiento
Es parte de
XLIII Jornadas de Automática : libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja)
Cita Bibliográfica
Jeuland, E.D., Río Ferreras, A. del, Chaves, D., Fidalgo Fernández, E., González Castro, V., Alegre Gutiérrez, E. (2022). Assessment of age estimation methods for forensic applications using non-occluded and synthetic occluded facial images. En XLIII Jornadas de Automática : libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja).
Editorial
Universidade da Coruña, Servizo de Publicacións
Fecha
2022
Resumen
[EN] Age estimation is a valuable forensic tool for criminal investigators since it helps to identify minors or possible
offenders in Child Sexual Exploitation Materials (CSEM). Nowadays, Deep Learning methods are considered
state-of-the-art for general age estimation. However, they have low performance in predicting the age of
minors and older adults because of the few examples of these age groups in the existing datasets. Moreover,
facial occlusion is used by offenders in certain CSEM, trying to hide the identity of the victims, which may also
affect the performance of age estimators. In this work, we assess the performance of six deep-learning-based
age estimators on non-occluded and occluded facial images. We selected FG-Net and APPA-REAL datasets
to evaluate the models under non-occluded conditions. To assess the models under occluded conditions, we
created synthetically occluded versions of the non-occluded datasets by drawing eye and mouth black masks to
simulate the conditions observed in some CSEM images. Experimental results showed that the evaluated age
estimators are affected more by eye occlusion than by mouth occlusion. Also, facial occlusion affects more
the accuracy of the age estimation of minors and the elderly compared to other age groups. We expect that
this study could become an initial benchmark for age estimation under non-occluded and occluded conditions,
especially for forensic applications like victim profiling on CSEM where age estimation is essential.
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