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Towards a reliable face recognition system.

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

Authors

Adamu Ali-Gombe

Johan Zwiegelaar



Contributors

Lazaros Iliadis
Editor

Plamen Parvanov Angelov
Editor

Christina Jayne
Editor

Elias Pimenidis
Editor

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

Conference Name 21st Engineering applications of neural networks conference 2020 (EANN 2020)
Conference Location Halkidiki, Greece
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
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

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