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Cyber Defence in the Age of AI, Smart Societies and Augmented Humanity pp 375–395Cite as

Deep Convolutional Neural Networks for Forensic Age Estimation: A Review

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Part of the Advanced Sciences and Technologies for Security Applications book series (ASTSA)

Abstract

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.

Keywords

  • Deep learning
  • CNN
  • Forensic investigation
  • Information fusion
  • Magnetic resonant imaging (MRI)
  • Dental X-ray

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Authors and Affiliations

  1. Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang, Malaysia

    Sultan Alkaabi & Salman Yussof

  2. Wolverhampton Cyber Research Institute (WCRI), University of Wolverhampton, Wolverhampton, UK

    Haider Al-Khateeb, Gabriela Ahmadi-Assalemi & Gregory Epiphaniou

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  1. Sultan Alkaabi
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  2. Salman Yussof
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  3. Haider Al-Khateeb
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  4. Gabriela Ahmadi-Assalemi
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  5. Gregory Epiphaniou
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Correspondence to Haider Al-Khateeb .

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Editors and Affiliations

  1. Northumbria University, London, UK

    Dr. Hamid Jahankhani

  2. Cyfortis, Worcester Park, Surrey, UK

    Stefan Kendzierskyj

  3. Open Innovation House, Saidot OY, ESPOO, Finland

    Nishan Chelvachandran

  4. Northumbria University, London, UK

    Jaime Ibarra

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Alkaabi, S., Yussof, S., Al-Khateeb, H., Ahmadi-Assalemi, G., Epiphaniou, G. (2020). Deep Convolutional Neural Networks for Forensic Age Estimation: A Review. In: Jahankhani, H., Kendzierskyj, S., Chelvachandran, N., Ibarra, J. (eds) Cyber Defence in the Age of AI, Smart Societies and Augmented Humanity. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-35746-7_17

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  • DOI: https://doi.org/10.1007/978-3-030-35746-7_17

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