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dc.contributor.advisorDornaika, Fadies
dc.contributor.advisorArganda Carreras, Ignacioes
dc.contributor.authorBelver Mielgo, Carlos
dc.description.abstractIn the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these
dc.subjectcomputer visiones
dc.subjectpattern recognitiones
dc.subjectface imagees
dc.subjectneural networkses
dc.subjectdeep learninges
dc.titleComparative study of human age estimation based on hand-crafted and deep face featureses
dc.rights.holderAttribution-NonCommercial-ShareAlike 4.0 International*

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Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International