A Review on Performance Enhancement of Deep Learning Based Face Detection System

Authors

  • Vinod M. Rathod Suresh Gyan Vihar University, Jaipur, Rajasthan, INDIA
  • Sohit Agarwal Suresh Gyan Vihar University, Jaipur, Rajasthan, INDIA
  • Rajendra B. Mohite Bharati Vidyapeeth College of Engineering, Kharghar, Navi Mumbai, Maharashtra, INDIA
  • Amit Jaykumar Chinchawade Sharad Institute of Technology College of Engineering, Yadrav (Ichalkaranji). Maharashtra, INDIA
  • Madhav Jagannath Salunkhe Kittiware IT Solutions Machine Learning Engineer Associate developer
  • Uttam P. Waghmode RAIT Institute, Nerul, Navi Mumbai, Maharashtra, INDIA

Keywords:

Convolutional Neural Network (CNN), Face recognition and Principal Component Analysis (PCA)

Abstract

It is difficult to identify faces since most individuals used masks during the COVID-19 epidemic. The NIST 2020 study states that the current state of the art approaches for facial recognition have an error rate of 20% to 50% when the mask is used. Lately, a plethora of academics have said that they would solve this issue utilizing CNN and other approaches. To positively identify people in certain instances, researchers combined characteristics from the mask-covered and unmasked parts of the face. There is a substantial mistake rate when using these two characteristics or scenarios to identify faces. To get around these problems, you may utilize tools like Principal Component Analysis (PCA).

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Published

23.02.2024

How to Cite

Rathod, V. M. ., Agarwal, S. ., Mohite, R. B. ., Chinchawade, A. J. ., Salunkhe, M. J. ., & Waghmode, U. P. . (2024). A Review on Performance Enhancement of Deep Learning Based Face Detection System. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 446–451. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4857

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

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