YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing

Cao, Zhicheng; Li, Wenlong; Zhao, Heng; Pang, Liaojun

doi:10.1007/978-3-031-20233-9_21

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…The Masked Face Detection Dataset (MFDD), the Real-World Masked Face Recognition Dataset (RMFRD), and the Synthetic Masked Face Recognition Dataset (SMFRD) are the three types of masked face dataset proposed by Huang et al [70], allowing for a more realistic evaluation of face classification algorithms. Cao et al [71] proposed a new dataset called Diverse Masked Faces and advised that the YOLOX model be modified with a new composite loss that combines CIoU and alpha-IoU losses and retains both benefits. Wang's mask creation module [72], on the other hand, used facial landmarks to generate more realistic and reliable masked faces for training in addition to using existing datasets.…”

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

View full text Add to dashboard Cite

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Medical Image Segmentation with Dual-Encoding and Multi-Level Feature Adaptive Fusion

Wu,

Yang,

Zhang

2024

Int. J. Patt. Recogn. Artif. Intell.

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Purpose: Accurate segmentation of medical images is critical for disease diagnosis, surgical planning and prognostic assessment. TransUNet, a hybrid CNN-Transformer-based method, extracts local features using CNN and compensates for the lack of long-range dependencies through a self-attention mechanism. However, the initial focus on extracting local features from specific regions impacts the generation of subsequent global features, thus constraining the model’s capacity to effectively capture a broader range of semantic information. Effective integration of local and global features plays a pivotal role in achieving precise and dense prediction. Therefore, we propose a novel hybrid CNN-Transformer-based method aimed at enhancing medical image segmentation. Approach: In this study, a dual-encoder parallel structure is used to enhance the feature representation of the input image. By introducing a multi-scale adaptive feature fusion module, a fine fusion of local features across perceptual domains is realized in the decoding process. The generalized convolutional block attention module helps to increase cross-channel interactions in layers with more channels, thus enabling the fusion of local features and global representations at different resolutions during the decoding process. Results: The proposed method achieves average DSC scores of 79.98%, 84.83% and 85.78% on the Synapse, ISIC2017 and Pediatric Pyelonephritis datasets, respectively. These scores are 2.5%, 0.56% and 0.42% higher than those of TransUNet. The best performance of 91.66% is observed on the ACDC dataset, representing improvements of 2.46% and 7.24% compared to HiFormer and DAE-Former, respectively. Conclusions: The experimental results show that the proposed model has a significant competitive advantage in terms of ACDC image segmentation performance.

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YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing

Cited by 2 publications

References 30 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

Medical Image Segmentation with Dual-Encoding and Multi-Level Feature Adaptive Fusion

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