Deep convolutional neural networks for forensic age estimation: A review
Forensic 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 cov...
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my.uniten.dspace-257252023-05-29T16:13:24Z Deep convolutional neural networks for forensic age estimation: A review Alkaabi S. Yussof S. Al-Khateeb H. Ahmadi-Assalemi G. Epiphaniou G. 57212311690 16023225600 55339456900 57208524615 36052693100 Forensic 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. Final 2023-05-29T08:13:24Z 2023-05-29T08:13:24Z 2020 Book Chapter 10.1007/978-3-030-35746-7_17 2-s2.0-85085219163 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085219163&doi=10.1007%2f978-3-030-35746-7_17&partnerID=40&md5=ad7f1f3d077118448629a22db4548bc8 https://irepository.uniten.edu.my/handle/123456789/25725 375 395 Springer Scopus |
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Forensic 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. |
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57212311690 |
| author_facet |
57212311690 Alkaabi S. Yussof S. Al-Khateeb H. Ahmadi-Assalemi G. Epiphaniou G. |
| format |
Book Chapter |
| author |
Alkaabi S. Yussof S. Al-Khateeb H. Ahmadi-Assalemi G. Epiphaniou G. |
| spellingShingle |
Alkaabi S. Yussof S. Al-Khateeb H. Ahmadi-Assalemi G. Epiphaniou G. Deep convolutional neural networks for forensic age estimation: A review |
| author_sort |
Alkaabi S. |
| title |
Deep convolutional neural networks for forensic age estimation: A review |
| title_short |
Deep convolutional neural networks for forensic age estimation: A review |
| title_full |
Deep convolutional neural networks for forensic age estimation: A review |
| title_fullStr |
Deep convolutional neural networks for forensic age estimation: A review |
| title_full_unstemmed |
Deep convolutional neural networks for forensic age estimation: A review |
| title_sort |
deep convolutional neural networks for forensic age estimation: a review |
| publisher |
Springer |
| publishDate |
2023 |
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1806427568534978560 |
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13.211869 |