Impact on Biometric Identification Systems of COVID-19

Guo, Yuheng

doi:10.1155/2021/3225687

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…As the world fought the heinous COVID-19 pandemic, traditional face-to-face engagements such as board meetings, press conferences, product introductions, and even family reunions have shifted to a more digital presence through video conversations based on the Internet. However, the use of masks has negatively affected the performance of existing face-based identification systems, as these systems have not been trained on masked face images [4].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%