Authors:
Deisy Chaves
1
;
2
;
Eduardo Fidalgo
1
;
2
;
Enrique Alegre
1
;
2
;
Francisco Jáñez-Martino
1
;
2
and
Rubel Biswas
1
;
2
Affiliations:
1
Department of Electrical, Systems and Automation, Universidad de Leon, León, Spain
;
2
Researcher at INCIBE (Spanish National Cybersecurity Institute), León, Spain
Keyword(s):
Age Estimation, Eye Occlusion, SSR-Net Model, CSEM, Forensic Images.
Abstract:
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.
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