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

Authors :
Damilola Akingbesote
Ying Zhan
Rytis Maskeliūnas
Robertas Damaševičius
Source :
Algorithms, Vol 16, Iss 6, p 292 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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.

Details

Language :
English
ISSN :
19994893
Volume :
16
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Algorithms
Publication Type :
Academic Journal
Accession number :
edsdoj.8eb328f4b7456199cb28e29429bdf6
Document Type :
article
Full Text :
https://doi.org/10.3390/a16060292