Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier

Abstract

Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology to address this problem and enable accurate identification and authentication in masked scenarios. In this paper, we propose a novel method that utilizes a combination of deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) for masked face recognition. Specifically, we use pretrained ssd-MobileNetV2 for detecting the presence and location of masks on a face and employ landmark and oval face detection to identify key facial features. The proposed method also utilizes RPCA to separate occluded and non-occluded components of an image, making it more reliable in identifying faces with masks. To optimize the performance of our proposed method, we use particle swarm optimization (PSO) to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. Our proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion.

A hybrid human recognition framework using machine learning and deep neural networks

“…Eman et al [ 19 ] introduced an innovative approach for masked face recognition, leveraging a multi-aspect methodology combining deep learning-based mask detection using pre-trained ssdMobileNetV2, landmark and oval face detection to identify crucial facial features, and robust principal component analysis (RPCA) to distinguish occluded and non-occluded components in facial images. To enhance performance, particle swarm optimization (PSO) is employed to optimize both KNN features and the number of neighbors (k) for K-nearest neighbors (KNN).…”

Lip-Reading Advancements: A 3D Convolutional Neural Network/Long Short-Term Memory Fusion for Precise Word Recognition

“…The study identified the Conv1D-LSTM architecture, augmented with dropout layers, as more effective, while the impact of feature scaling, principal component analysis (PCA), and feature selection methods was highlighted. In the realm of facial recognition, Taha et al [11] introduced a novel approach to recognize faces with masks, integrating mask detection, landmark detection, and oval face detection. This was performed using robust principal component analysis (RPCA) and a pretrained ssd-MobileNetV2 model for mask detection, and the features were optimized with the Gazelle Optimization Algorithm (GOA).…”

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“…Eman et al [19] introduced an innovative approach for masked face recognition, leveraging a multi-aspect methodology combining deep learning-based mask detection using pre-trained ssdMobileNetV2, landmark and oval face detection to identify crucial facial features, and robust principal component analysis (RPCA) to distinguish occluded and non-occluded components in facial images. To enhance performance, particle swarm optimization (PSO) is employed to optimize both KNN features and the number of neighbors (k) for K-nearest neighbors (KNN).…”