article · Scientific Journal for Damietta Faculty of Science/Scientific Journal for Damietta Faculty of Science
A hybrid method has been developed to reliably recognise individuals wearing masks. The system uses a pretrained ssd-MobileNetV2 model to detect the presence and exact placement of masks, alongside landmark and oval face detection to identify essential facial features. To handle occlusion, robust principal component analysis separates the covered parts of an image from the uncovered parts. The classification stage relies on K-nearest neighbours, which is optimised using the Gazelle Optimization Algorithm to select features and tune the neighbourhood parameter k. Tested across challenging environments, the approach achieved a 97 percent recognition rate, demonstrating superior accuracy and resilience to facial occlusion compared to existing alternatives.
Standard automated identity verification systems often struggle when people wear protective face coverings. By reliably identifying masked individuals without requiring them to remove their masks, this technology supports public health protocols while maintaining security. It ensures that automated identification remains functional in high-traffic, health-sensitive environments.
The method targets security systems, access control facilities, and public health monitoring environments. The technology appears to be applied and tested at an algorithmic stage, having achieved 97 percent accuracy in experimental evaluations. Transition to commercial software products will require moving the algorithms from experimental testing into integrated surveillance or security hardware.
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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.
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DOI: 10.21608/sjdfs.2023.222524.1117
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