Convolutional Neural Network for Skull Recognition

Authors

  • Badr Lahasan Department of Computer Programming Faculty of Education– Shabwa, University of Aden, Shabwah, Yemen Faculty of Computer And Information Technology, University of Shabwah,. Shabwah, Yemen
  • Hussein Samma School of Computing, Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru, Malaysia

DOI:

https://doi.org/10.11113/ijic.v12n1.347

Keywords:

Deep learning, face skull identification, CNN model

Abstract

Automatic skull identification systems play a vital role for forensic law authorities to recognize victim identity.  Motivated by potential applications of these kinds of systems, this research aims to apply a pre-trained deep convolutional neural network (CNN) for face skull recognition. Basically, the unknown skull image is fed to a pre-trained CNN network to extract a 1D feature vector, and then it will be matched with photos at database agencies to identify the closest match. To validate the proposed skull recognition system, it has been applied for a total of 13 skulls, and the reported results indicated a good was achieved. In addition, various CNN architectures were investigated, including shallow, medium, and deep CNN models. The best performance was reported from the shallow CNN model with a 92% recognition rate.  

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Published

2021-11-16

How to Cite

Lahasan, B. ., & Samma, H. . (2021). Convolutional Neural Network for Skull Recognition. International Journal of Innovative Computing, 12(1), 55–58. https://doi.org/10.11113/ijic.v12n1.347

Issue

Section

Computer Science