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

Authors

  • 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 | https://doi.org/10.48084/etasr.7002

Abstract

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.

Keywords:

ANOVA, CNN, face recognition, DLA, MI

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References

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How to Cite

[1]
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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