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


  • 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


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


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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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



Research Article