Research Article
Comparative Analysis of Face Recognition Algorithms for Facial Recognition in Diverse Environments
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.
Keywords
References
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- [18] Schroff, F., Kalenichenko, D., Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceed-ings of the IEEE conference on computer vision and pattern recognition (pp. 815-823).
- [19] Suwarno, S., & Kevin, K. (2020). Analysis of face recognition algorithm: Dlib and opencv. Journal of Informatics and Telecom-munication Engineering, 4(1), 173-184.
- [20] Baltrušaitis, T., Robinson, P., Morency, L. P. (2016, March). Openface: an open source facial behavior analysis toolkit. In 2016 IEEE winter conference on applications of computer vision (WACV) (pp. 1-10). IEEE.
- [21] Deng, J., Guo, J., Xue, N., Zafeiriou, S. (2019). Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4690-4699).
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- [23] Nguyen, H. V., Bai, L. (2010, November). Cosine similarity metric learning for face verification. In Asian conference on comput-er vision (pp. 709-720). Berlin, Heidelberg: Springer Berlin Heidelberg.
Abstract
References
- [1] Kortli, Y., Jridi, M., Al Falou, A., Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 342.
- [2] Elngar, A. A., Kayed, M. (2020). Vehicle security systems using face recognition based on internet of things. Open Computer Science, 10(1), 17-29.
- [3] Tribuana, D., Hazriani, H., Arda, A. L. (2024). Face recognition for smart door security access with convolutional neural network method. TELKOMNIKA (Telecommunication Computing Electronics and Control), 22(3), 702-710.
- [4] Ben Fredj, H., Bouguezzi, S., Souani, C. (2021). Face recognition in unconstrained environment with CNN. The Visual Com-puter, 37(2), 217-226.
- [5] Budiman, A., Yaputera, R. A., Achmad, S., Kurniawan, A. (2023). Student attendance with face recognition (LBPH or CNN): Systematic literature review. Procedia Computer Science, 216, 31-38.
- [6] Li, L., Mu, X., Li, S., Peng, H. (2020). A review of face recognition technology. IEEE access, 8, 139110-139120. [7] Geitgey, A. (n.d.). face_recognition. GitHub repository. Retrieved June 14, 2024, from https://github.com/ageitgey/face_recognition
- [8] Exadel Inc. (n.d.). CompreFace. GitHub repository. Retrieved June 14, 2024, from https://github.com/exadel-inc/CompreFace
- [9] Serengil, S., Özpınar, A. (2024). A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules. Bilişim Teknolojileri Dergisi, 17(2), 95-107.
- [10] Komlavi, A. A., Chaibou, K., Naroua, H. (2022). Comparative study of machine learning algorithms for face recognition. Revue Africaine de Recherche En Informatique et Mathématiques Appliquées, 40.
- [11] Radhika, C., Pathak, B. V. (2018). Face recognition based attendance system using machine learning algorithms. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). (" 2018 Second International Conference on Intelligent Computing and…"). IEEE.
- [12] Güven, Ö. (2021). An Application on Identification With The Face Recognition System. Electronic Letters on Science and En-gineering, 17(2), 198-207.
- [13] Yakovleva, O., Kovtunenko, A., Liubchenko, V., Honcharenko, V., Kobylin, O. (2023). Face Detection for Video Surveillance-based Security System. In COLINS (3) (pp. 69-86).
- [14] Mohammed, M. G., Melhum, A. I. (2020). Implementation of HOG feature extraction with tuned parameters for human face detection. International Journal of Machine Learning and Computing, 10(5), 654-661.
- [15] Wu, C., Zhang, Y. (2021). MTCNN and FACENET based access control system for face detection and recognition. Automatic Control and Computer Sciences, 55, 102-112.
- [16] Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S. (2019). Retinaface: Single-stage dense face localisation in the wild. arXiv preprint arXiv:1905.00641.
- [17] Shi, Y., Yu, X., Sohn, K., Chandraker, M., Jain, A. K. (2020). Towards universal representation learning for deep face recogni-tion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6817-6826).
- [18] Schroff, F., Kalenichenko, D., Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceed-ings of the IEEE conference on computer vision and pattern recognition (pp. 815-823).
- [19] Suwarno, S., & Kevin, K. (2020). Analysis of face recognition algorithm: Dlib and opencv. Journal of Informatics and Telecom-munication Engineering, 4(1), 173-184.
- [20] Baltrušaitis, T., Robinson, P., Morency, L. P. (2016, March). Openface: an open source facial behavior analysis toolkit. In 2016 IEEE winter conference on applications of computer vision (WACV) (pp. 1-10). IEEE.
- [21] Deng, J., Guo, J., Xue, N., Zafeiriou, S. (2019). Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4690-4699).
- [22] Malkauthekar, M. D. (2013, October). Analysis of euclidean distance and manhattan distance measure in face recognition. In Third International Conference on Computational Intelligence and Information Technology (CIIT 2013) (pp. 503-507). IET.
- [23] Nguyen, H. V., Bai, L. (2010, November). Cosine similarity metric learning for face verification. In Asian conference on comput-er vision (pp. 709-720). Berlin, Heidelberg: Springer Berlin Heidelberg.
There are 22 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Engineering (Other) |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | December 11, 2024 |
| Publication Date | December 31, 2024 |
| Submission Date | June 26, 2024 |
| Acceptance Date | August 1, 2024 |
| Published in Issue | Year 2024 Volume: 3 Issue: 2 |
