Convolutional Neural Network for Skull Recognition

Convolutional Neural Network for Skull Recognition


  • Hussein Salem Ali Samma ekolah Komputeran Fakulti Kejuruteraan Universiti Teknologi Malaysia
  • Bader Lahasan


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

Samma, H. S. A., & Lahasan, B. . (2021). Convolutional Neural Network for Skull Recognition: Convolutional Neural Network for Skull Recognition. International Journal of Innovative Computing, 12(1). Retrieved from



Computer Science