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Towards a Reliable Face Recognition System

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

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.

Supported by InnovateUK, Mintra Group and Robert Gordon University.

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Notes

  1. 1.

    https://github.com/davisking/dlib.

  2. 2.

    https://pypi.org/project/mtcnn/.

  3. 3.

    https://github.com/pjreddie/darknet.

  4. 4.

    https://github.com/deepinsight/insightface.

  5. 5.

    https://github.com/WeidiXie/Keras-VGGFace2-ResNet50.

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Authors and Affiliations

  1. Robert Gordon University, Aberdeen, UK

    Adamu Ali-Gombe & Eyad Elyan

  2. Mintra Group, Oslo, Norway

    Johan Zwiegelaar

Authors
  1. Adamu Ali-Gombe
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  2. Eyad Elyan
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  3. Johan Zwiegelaar
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Corresponding author

Correspondence to Adamu Ali-Gombe .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Ali-Gombe, A., Elyan, E., Zwiegelaar, J. (2020). Towards a Reliable Face Recognition System. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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