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Journals / Cukurova University Journal of Natural and Applied Sciences / 2024 / Volume: 3 Issue: 2

Comparative Analysis of Face Recognition Algorithms for Facial Recognition in Diverse Environments

Üsame Durak(Biges Güvenli Hayat Teknolojiler A.Ş)0000-0003-1723-3444
Ayşegül Ceren Koç(Biges Güvenli Hayat Teknolojiler A.Ş.)0000-0001-5519-3231
Hüseyin Daş(Biges Güvenli Hayat Teknolojiler A.Ş.)0000-0003-4254-413X
Oğuzhan Karahan(Biges Güvenli Hayat Teknolojiler A.Ş.)0000-0002-8571-5528
M. Fatih Kılıç(Biges Güvenli Hayat Teknolojiler A.Ş.)0000-0002-5596-7097
Mehmet Fatih Akay(ÇUKUROVA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0003-0780-0679
Pages
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Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experimental results show that both algorithms achieve high accuracy in controlled environments, with Dlib showing greater robustness in complex scenarios.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Facial recognition technology has evolved significantly over the last five decades and plays a central role in various applications such as biometrics, information security, access control, law enforcement and surveillance.
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Key Findings & Data:In this study, the performance of two face recognition algorithms, Dlib and FaceNet, is evaluated using datasets obtained from video recordings in different environments.
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Conclusion & Impact:The experimental results show that both algorithms achieve high accuracy in controlled environments, with Dlib showing greater robustness in complex scenarios.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Face recognition · FaceNet · Dlib

Abstract

Facial recognition technology has evolved significantly over the last five decades and plays a central role in various applications such as biometrics, information security, access control, law enforcement and surveillance. In this study, the performance of two face recognition algorithms, Dlib and FaceNet, is evaluated using datasets obtained from video recordings in different environments. The Dlib algorithm uses the Histogram of Oriented Gradients (HOG) method for face detection, while FaceNet uses the Multi-Task Cascaded Convolutional Neural Network (MTCNN). The experimental results show that both algorithms achieve high accuracy in controlled environments, with Dlib showing greater robustness in complex scenarios. This study makes an important contribution to this topic by presenting a comparative analysis of the face recognition performance of the OpenFace, ArcFace, Exadel, and Dlib methods under different environmental conditions and scenarios. The results show that while the tested methods achieve high accuracy in controlled environments, their performance differs in more com-plex environments.In the results, OpenFace and ArcFace showed lower success rates than the other two algorithms. In particu-lar, Dlib proved superior in dynamic and challenging scenarios, achieving an overall accuracy of 96.1% compared to 94.6% for Exadel. Exadel, on the other hand, performed slightly better in certain controlled environments, highlighting its potential strength in certain applications. These results emphasize the importance of selecting the appropriate algorithm based on the specific environmental conditions and requirements of the application. This research not only improves our understanding of the performance characteristics of leading facial recognition technologies, but also provides practical insights into their use in real-world applications.

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Cite This Article

Durak, Ü., Koç, A. C., Daş, H., Karahan, O., Kılıç, M. F., Akay, M. F. (2024). Comparative Analysis of Face Recognition Algorithms for Facial Recognition in Diverse Environments. Cukurova University Journal of Natural and Applied Sciences, Volume: 3 Issue: 2. https://doi.org/10.70395/cunas.1504238

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