Developed artificial neural network based on human face recognition

Maryam Mahmood Hussein, Ammar Hussein Mutlag, Hussain Shareef

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Face recognition has become one of the most important challenging problems in personal computer-human interaction, video observation, and biometric. Many algorithms have been developed in recent years. These algorithms are not sufficiently robust to address the complex images. Therefore, this paper proposes a soft computing algorithm based on face recognition. One of the most promising soft computing algorithms which is back-propagation artificial neural network (BP-ANN) has been proposed. The proposed BP-ANN has been developed to improve the performance of face recognition. The implementation of the developed BP-ANN has been achieved using the MATLAB environment. The developed BP-ANN requires supervised training to learn how to anticipate results from the desired data. The BP-ANN has been developed to recognize 10 persons. Ten images have been used for each person. Therefore, 100 images have been utilized to train the developed BP-ANN. In this research 50 images have been used for testing purpose. The results show that the developed BP-ANN has produced a success ratio of 82 %.

Original languageEnglish
Pages (from-to)1279-1285
Number of pages7
JournalIndonesian Journal of Electrical Engineering and Computer Science
Issue number3
Publication statusPublished - 2019


  • Back-propagation artificial
  • Face detection
  • Face recognition
  • Neural network (BP-ANN)
  • Viola-Jones algorithm

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering


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