Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42117
|Title:||Evaluation of local descriptors and CNNs for non-adult detection in visual content||Authors:||Castrillón-Santana, Modesto
Travieso-González, Carlos M.
Alonso-Hernández, Jesús B.
|UNESCO Clasification:||120304 Inteligencia artificial||Keywords:||MSC: 41A05
Score level fusion
|Issue Date:||2018||Journal:||Pattern Recognition Letters||Abstract:||The recent evolution of storage devices, digital embedded cameras and the Internet have collaterally allowed sexual predators to take advantage of these technological breakthroughs to gather illegal media, which is exhibited uncensored through Peer-to-Peer file sharing networks. In this paper, we are particularly concerned about the increasing availability of Child Abuse Material. Therefore, we have explored alternatives to detect non-adults in visual content. Initially, different age estimations and underage detection techniques are reviewed by analyzing existing datasets. Finally, several local descriptors and Convolutional Neural Networks for underage detection are evaluated. The experimental results obtained for a large dataset that combines collections such as FG-Net, Adience, GenderChildren, The Image of Groups and Boys2Men evidence the complementary information contained in both local descriptors and neural networks, as their fusion boosts the accuracy of non-adult detection to over 93%.||URI:||http://hdl.handle.net/10553/42117||ISSN:||0167-8655||DOI:||10.1016/j.patrec.2017.03.016||Source:||Pattern Recognition Letters [ISSN 0167-8655], v. 113, p. 10-18|
|Appears in Collections:||Artículos|
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