Automated Gender and Facial Identification Using a Novel Evolutionary Algorithm

Authors

  • Deeksha Chaudhary Maharishi University of Information Technology, Lucknow, India -226036
  • Manpreet Singh Chitkara University, Rajpura, Punjab, India
  • Murugan R. Jain (Deemed to be University), Bangalore, Karnataka, India
  • Sweta Kumari Department of ISME, ATLAS SkillTech University, Mumbai, Maharashtra, India
  • Mohammad Asif Iqbal Department of Computer Science & Application, Vivekananda Global University, Jaipur

Keywords:

Automated, gender, facial identification, particle swarm-optimized improved genetic algorithm (PSO-IGA)

Abstract

Systems for facial identification concentrate on comparing a person's face to a recognized identity or figuring out whether a face is unfamiliar. In order to assess distinctive facial traits like eye distance, nose shape, and face contours, these systems use advanced algorithms. Numerous uses for facial recognition exist, from access control and law enforcement to tailored services. It provides improved security safeguards, effective identity verification, and frictionless user interactions. However, examining several facial features and the way they work together might assist in determining a person's gender from face images. In this paper, we propose an approach particle swarm-optimized improved genetic algorithm (PSO-IGA). This approach is used for automated gender and facial identification. There are 840 men and 917 women in the audience dataset. For feature extraction, Principal Component Analysis (PCA) approaches are employed. The experiment parameters have been performed accuracy (98.2%), precision (95.1%), specificity (93.3%), recall (92.7%), and sensitivity (96.4%). When compared to the existing approach, the suggested methods are highly accurate. In summary, automated facial and gender recognition technologies have proven they have the power to change a number of sectors. They provide improved security, individualized service, and quick identity verification.

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Published

24.03.2024

How to Cite

Chaudhary, D. ., Singh, M. ., R., M. ., Kumari, S. ., & Iqbal, M. A. . (2024). Automated Gender and Facial Identification Using a Novel Evolutionary Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 814–821. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5214

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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