Open Access

ARTICLE

Automated Video-Based Face Detection Using Harris Hawks Optimization with Deep Learning

by Latifah Almuqren¹, Manar Ahmed Hamza^2,*, Abdullah Mohamed³, Amgad Atta Abdelmageed²

1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
3 Research Centre, Future University in Egypt, New Cairo, 11845, Egypt

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2023, 75(3), 4917-4933. https://doi.org/10.32604/cmc.2023.037738

Received 15 November 2022; Accepted 23 February 2023; Issue published 29 April 2023

Abstract

Face recognition technology automatically identifies an individual from image or video sources. The detection process can be done by attaining facial characteristics from the image of a subject face. Recent developments in deep learning (DL) and computer vision (CV) techniques enable the design of automated face recognition and tracking methods. This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking (HHODL-AFDT) method. The proposed HHODL-AFDT model involves a Faster region based convolution neural network (RCNN)-based face detection model and HHO-based hyperparameter optimization process. The presented optimal Faster RCNN model precisely recognizes the face and is passed into the face-tracking model using a regression network (REGN). The face tracking using the REGN model uses the features from neighboring frames and foresees the location of the target face in succeeding frames. The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work. The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60% and 88.08% under PICS and VTB datasets, respectively.

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Keywords

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