A Novel Hybrid Approach to Masked Face Recognition using Robust PCA and GOA Optimizer
Taha, M., Mostafa, T., Abd El-Rahman, T. (2023). A Novel Hybrid Approach to Masked Face Recognition using Robust PCA and GOA Optimizer. EKB Journal Management System, 13(3), 25-35. doi: 10.21608/sjdfs.2023.222524.1117
Mohammed Eman Taha; Tarek Mostafa; Tarek Abd El-Hafeez Abd El-Rahman. "A Novel Hybrid Approach to Masked Face Recognition using Robust PCA and GOA Optimizer". EKB Journal Management System, 13, 3, 2023, 25-35. doi: 10.21608/sjdfs.2023.222524.1117
Taha, M., Mostafa, T., Abd El-Rahman, T. (2023). 'A Novel Hybrid Approach to Masked Face Recognition using Robust PCA and GOA Optimizer', EKB Journal Management System, 13(3), pp. 25-35. doi: 10.21608/sjdfs.2023.222524.1117
Taha, M., Mostafa, T., Abd El-Rahman, T. A Novel Hybrid Approach to Masked Face Recognition using Robust PCA and GOA Optimizer. EKB Journal Management System, 2023; 13(3): 25-35. doi: 10.21608/sjdfs.2023.222524.1117

A Novel Hybrid Approach to Masked Face Recognition using Robust PCA and GOA Optimizer

Scientific Journal for Damietta Faculty of Science
Volume 13, Issue 3, December 2023, Page 25-35  XML PDF (534.28 K)
Document Type: Original articles
DOI: 10.21608/sjdfs.2023.222524.1117
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Authors
Mohammed Eman Taha1; Tarek Mostafa2; Tarek Abd El-Hafeez Abd El-Rahman email orcid 3
1Computer science, faculty of computer and AI, Bani swef University, Bani swef, Egypt
2Faculty of Computers and Artificial Intelligence, University of Sadat City, Egypt
3Department of computer science, Faculty of science, Minia University
Abstract
This paper presents a novel method for recognizing faces with masks. The proposed method integrates deep learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) to accurately identify and authenticate individuals wearing masks. A pretrained ssd-MobileNetV2 model is utilized to detect the presence and location of masks on a face, while landmark and oval face detection are used to identify and extract important facial features. RPCA is applied to separate the occluded and non-occluded components of an image, making the method more reliable in identifying faces with masks. To further optimize the performance of the proposed method, the Gazelle Optimization Algorithm (GOA) is used to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that the proposed method outperforms existing methods in terms of accuracy and robustness to occlusion, achieving a recognition rate of 97%. This represents a significant improvement over existing methods for masked face recognition. The proposed method has the potential to be applied in a wide range of real-world scenarios, such as security systems, access control, and public health measures. The results of this study demonstrate that the integration of deep learning-based mask detection, landmark and oval face detection, and RPCA can improve the accuracy and reliability of masked face recognition, even in challenging and complex environments. The proposed method can be further improved and extended in future research to address other challenges in this field.
Keywords
Face masks problem; robust principal component analysis; gazelle optimization algorithm; KNN
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