Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence


  • Hind Moutaz Al-Dabbas Department of Computer Science, College of Education for Pure Science (Ibn Al-Haitham), Iraq | University of Baghdad, Iraq
  • Raghad Abdulaali Azeez Information Technology Unit, College of Education Ibn-Rushd for Human Sciences, Iraq | University of Baghdad, Iraq
  • Akbas Ezaldeen Ali Department of Computer Science, University of Technology, Iraq
Volume: 14 | Issue: 2 | Pages: 13706-13713 | April 2024 |


In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimensional Convolutional Neural Network Hybrid Model (1D-CNNHM). The MUCT database was considered for training and evaluation. The performance, in terms of classification, of the J48 model reached 96.01% accuracy whereas the DL model that merged LDA with MI and ANOVA reached 100% accuracy. Comparing the proposed models with other works reflects that they are performing very well, with high accuracy and low processing time.


ANOVA, CNN, face recognition, DLA, MI


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How to Cite

H. M. Al-Dabbas, R. A. Azeez, and A. E. Ali, “Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13706–13713, Apr. 2024.


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