Convolutional Neural Network for Skull Recognition


  • 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



Deep learning, face skull identification, CNN model


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.  




How to Cite

Lahasan, B. ., & Samma, H. . (2021). Convolutional Neural Network for Skull Recognition. International Journal of Innovative Computing, 12(1), 55–58.



Computer Science