Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition

Neto, Pedro C.; Pinto, João Ribeiro; Damer, Naser; Sequeira, Ana F.; Cardoso, Jaime S.

doi:10.1109/access.2022.3199014

Cited by 17 publications

(3 citation statements)

References 61 publications

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“…Face recognition algorithms have evolved rapidly over the years due to a variety of causes [48]. Researchers have researched and created a variety of algorithms for occluded face identification in response to the unexpected aspects encountered in real world circumstances [49]. Zhao suggested a consistent subdecision network to obtain subdecisions that correspond to different facial areas and constraining subdecisions using weighted bidirectional KL divergence to focus the network on the upper faces without occlusion [50].…”

Section: Methods Recognition Ratementioning

confidence: 99%

Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques

Akingbesote,

Zhan,

Maskeliūnas

et al. 2023

Algorithms

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The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance accuracy and computation time, and data preprocessing techniques to address the issue of masked faces. The evaluation results demonstrate that the model achieves high accuracy on both masked and unmasked faces, outperforming existing models in the literature. The findings of this study have implications for improving the performance of face recognition systems in real-world scenarios where mask-wearing is prevalent. The results of this study show that the Pareto optimization allowed improving the overall accuracy over the 94% achieved by the original FaceNet variant, which also performed similarly to the ArcFace model during testing. Furthermore, a Pareto-optimized model no longer has a limitation of the model size and is much smaller and more efficient version than the original FaceNet and derivatives, helping to reduce its inference time and making it more practical for use in real-life applications.

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Section: Methods Recognition Ratementioning

confidence: 99%

Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques

Akingbesote,

Zhan,

Maskeliūnas

et al. 2023

Algorithms

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show abstract

“…In recent years after the pandemic, many researchers devoted their efforts to finding a solution to the problem of mask confusion and its impact on the face recognition task [ 4 , 5 ]. In this study [ 6 ], Neto et al aim to evaluate the different approaches followed for both Masked Face Recognition (MFR) and Occluded Face Recognition (OFR), find linked details about the two conceptually similar research directions, and understand future directions for both topics. The analysis presented sustains the interoperable deployability of MFR methods on OFR data sets when the occlusions are of a reasonable size.…”

Section: Related Workmentioning

confidence: 99%

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

Eman

Mahmoud

Ibrahim

et al. 2023

Sensors

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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.

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“…Face mask detection system is a computer vision application designed to identify whether or not individuals are wearing face masks [1,2]. It can be applied in a wide range of fields and scenarios, such as hospitals, schools, public transportation, and other establishments for ensuring public safety while maintaining strict health guidelines [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid ROI Extraction Approach for Mask and Unmask Facial Recognition System using Light-CNN

Ahmed,

S. Alghareb

2024

IJCDS

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In recent years, deep learning-based algorithms have been immensely employed and tested in a variety of real-world applications. The efficacy of such algorithms has been thoroughly examined in a practical setting. In this paper, CNN-based deep learning approaches are utilized to recognize faces in real-time to identify faces with and without mask. We employ pre-trained algorithms (YOLOv2 and SSD) to identify people wearing a face mask, which enables a machine to perform recognition tasks while evolving through a learning method. Meanwhile, if there is more than one person in the scene, the one with the max score will be selected for classification. Thus, a hybrid approach that combines YOLOv2 and SSD algorithms to work in parallel is developed for masked-face extraction. Likewise, the Viola-Jones algorithm is used here to detect faces without mask and randomly select a single region of interest (ROI) to be stored for classification. All pre-processing algorithms work separately in parallel as reconstruction steps for preprocessing to crop the ROI and store images for training and testing dataset. Followed by developing a lightweight computational complexity CNN model for face mask recognition to identify whether the selected person's face is wearing a mask or not. The dataset contains numerous variations in appearance and viewpoint to capture different scenarios with and without mask faces. On average, the proposed face mask detection architecture realizes recall and F1 score of 98.3% and 98.31%, respectively. The training performance, on the other hand, has improved by 32.1% for training time as compared to AlexNet and 40.43% of storage space (model size) reduction compared to EfficientNet-B0. The presented framework architecture is an efficient face mask and unmask detector and can be employed as a robust medical assistant face detector in the healthcare sector for automated tracking of a patient, visitor, or staff member wearing a mask or not.

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Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition

Cited by 17 publications

References 61 publications

Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques

Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques

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

A Hybrid ROI Extraction Approach for Mask and Unmask Facial Recognition System using Light-CNN

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