Towards a reliable face recognition system.

Ali-Gombe, Adamu; Elyan, Eyad; Zwiegelaar, Johan

Abstract

Face Recognition (FR) is an important area in computer vision with many applications such as security and automated border controls. The recent advancements in this domain have pushed the performance of models to human-level accuracy. However, the varying conditions in the real-world expose more challenges for their adoption. In this paper, we investigate the performance of these models. We analyze the performance of a cross-section of face detection and recognition models. Experiments were carried out without any preprocessing on three state-of-the-art face detection methods namely HOG, YOLO and MTCNN, and three recognition models namely, VGGface2, FaceNet and Arcface. Our results indicated that there is a significant reliance by these methods on preprocessing for optimum performance.

Citation

ALI-GOMBE, A., ELYAN, E. and ZWIEGELAAR, J. 2020. Towards a reliable face recognition system. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, 2. Cham: Springer [online], pages 304-316. Available from: https://doi.org/10.1007/978-3-030-48791-1_23

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 21st Engineering applications of neural networks conference 2020 (EANN 2020)
• Start Date: Jun 5, 2020
• End Date: Jun 7, 2020
• Acceptance Date: Mar 29, 2020
• Online Publication Date: May 28, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Jun 23, 2020
• Publicly Available Date: May 29, 2021
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 304-316
• Series Title: Proceedings of the International Neural Networks Society
• Series Number: 2
• Series ISSN: 2661-8141
• Book Title: Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020): proceedings of the EANN 2020
• ISBN: 9783030487904
• DOI: https://doi.org/10.1007/978-3-030-48791-1_23
• Keywords: Face detection; Face recognition; Deep learning; YOLO
• Public URL: https://rgu-repository.worktribe.com/output/936594