Yerel İkili Desenler Histogramları ile Covid-19 Tanılı Kişiler Üzerinde Kimlik Analizi ve Bildiri Sistemi

Karadağ, Nuray; Aydın, Hakan

doi:10.30855/gmbd.2020.03.01

Cited by 1 publication

(3 citation statements)

References 23 publications

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“…While One Person's Face Is Masked (Özdemir, 2019) 80 While One Person's Face Is Masked (Atasoy, 2018) 80 As shown in Table 3, the success rate of 85% obtained as a result of this study is slightly lower than the success rate of 84.5% in the study of Karadağ (2020). While Karadağ's study showed that he was able to achieve slightly higher accuracy in recognizing masked faces, this study still achieved an acceptable level of recognition success of 85%.…”

Section: Figure 6 Evaluation Of Performed Experimentsmentioning

confidence: 66%

“…It provides insights into the current challenges and future potential of facial recognition technologies in the context of the COVID-19 period. In the study conducted by Karadağ (2020), face recognition was performed by detecting the face with the Haar-Cascades classifier and then using the LBPH (Local Binary Patterns Histograms) method. Van Natta et al (2020) examined the rise and regulation of thermal facial recognition technology during the COVID-19 pandemic.…”

Section: Related Workmentioning

confidence: 99%

“…The experimental results we obtained in the study were also compared with the results of similar studies in the literature (Table 3). (Karadağ, 2020) 84.5…”

Section: Figure 6 Evaluation Of Performed Experimentsmentioning

confidence: 99%

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Performance Evaluation of Face Recognition System (FRS) Developed with Haar Cascade and MongoDB Integration in Recognition of Covered Faces

Yıldız,

Güney,

Aydın

2023

Bilgisayar Bilimleri Ve Teknolojileri Dergisi

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Although traditional face recognition systems (FRS) can detect with a certain success rate whether a mask is worn, they may fail due to the fact that most of the faces of the people who wear masks are covered. The difficulties arising from the fact that a significant part of the faces of individuals wearing masks are covered limits the performance of existing FRSs. In this research, it is aimed to integrate the Haar Cascade method with the MongoDB database in real time using the OpenCV library for mask-wearing face recognition and to demonstrate its performance with extensive experiments. In the experiments, the data set created within the scope of this study from realistic face images, in which most of the masked faces are covered, was used. Our research has shown that the accuracy of face recognition is 85% for masked faces, 61% for unmasked faces, and 41% when half of the face is covered by a different object. It is considered that this study will contribute to the literature in terms of providing a more effective and applicable mask detection solution by combining the Haar Cascade method with real-time database management integration.

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Section: Figure 6 Evaluation Of Performed Experimentsmentioning

confidence: 66%

Section: Related Workmentioning

confidence: 99%