Publication:
Deep convolutional neural networks for forensic age estimation: A review

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dc.citedby1
dc.contributor.authorAlkaabi S.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorAl-Khateeb H.en_US
dc.contributor.authorAhmadi-Assalemi G.en_US
dc.contributor.authorEpiphaniou G.en_US
dc.contributor.authorid57212311690en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid55339456900en_US
dc.contributor.authorid57208524615en_US
dc.contributor.authorid36052693100en_US
dc.date.accessioned2023-05-29T08:13:24Z
dc.date.available2023-05-29T08:13:24Z
dc.date.issued2020
dc.description.abstractForensic age estimation is usually requested by courts, but applications can go beyond the legal requirement to enforce policies or offer age-sensitive services. Various biological features such as the face, bones, skeletal and dental structures can be utilised to estimate age. This article will cover how modern technology has developed to provide new methods and algorithms to digitalise this process for the medical community and beyond. The scientific study of Machine Learning (ML) have introduced statistical models without relying on explicit instructions, instead, these models rely on patterns and inference. Furthermore, the large-scale availability of relevant data (medical images) and computational power facilitated by the availability of powerful Graphics Processing Units (GPUs) and Cloud Computing services have accelerated this transformation in age estimation. Magnetic Resonant Imaging (MRI) and X-ray are examples of imaging techniques used to document bones and dental structures with attention to detail making them suitable for age estimation. We discuss how Convolutional Neural Network (CNN) can be used for this purpose and the advantage of using deep CNNs over traditional methods. The article also aims to evaluate various databases and algorithms used for age estimation using facial images and dental images. � 2020, Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-35746-7_17
dc.identifier.epage395
dc.identifier.scopus2-s2.0-85085219163
dc.identifier.spage375
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085219163&doi=10.1007%2f978-3-030-35746-7_17&partnerID=40&md5=ad7f1f3d077118448629a22db4548bc8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25725
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleAdvanced Sciences and Technologies for Security Applications
dc.titleDeep convolutional neural networks for forensic age estimation: A reviewen_US
dc.typeBook Chapteren_US
dspace.entity.typePublication
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