Efficient Techniques For Human Face Occlusions Detection and Extraction

Sghaier, Souhir; Elfakki, Abir Osman

doi:10.1109/widstaif52235.2021.9430214

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…To avoid being tracked by CCTV, for example, when captured by law enforcement and subsequently escaping from their custody, the vast majority of criminal suspects choose to cover some of their faces with hats or masks [14]. Several researchers have studied face occlusion technology for quite some time [15] with an eye on meeting the needs of real-world security scenarios [16,17], and have made several different attempts to make the technology more user-friendly, as the ethical use of face recognition in areas such as law enforcement investigations requires a set of clear criteria to ensure that this technology is trustworthy and safe [18]. Deep forgery detection techniques are learning-based systems that rely on data to a certain degree.…”

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%