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Towards an Effective Approach for Face Recognition with DCGANs Data Augmentation

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12509))

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Abstract

Deep Convolutional Neural Networks (DCNNs) are widely used to extract high-dimensional features in various image recognition tasks [1] and have shown significant performance in face recognition. However, accurate real-time face recognition remains a challenge, mainly due to the high computation cost associated with the use of DCNNs and the need to balance precision requirements with time and resource restrictions. Besides, the supervised training process of DCNNs requires a large number of labeled samples. Aiming at solving the problem of data insufficiency, this study proposes a Deep Convolutional Generative Adversarial Net (DCGAN) based solution to increase the face dataset by generating synthetic images. Our proposed face recognition approach is based on FaceNet model. First, we perform face detection using MTCNN. After, a 128-D face embedding is extracted to quantify each face and a Support Vector Machine (SVM) is applied on top of the embeddings to recognize faces. In the experiment part, both LFW database and Chokepoint video database showed that our proposed approach with DCGANs data augmentation has improved the face recognition performance.

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Author information

Authors and Affiliations

  1. Laboratoire MIRACL, Université de Sfax, Sfax, Tunisie

    Sirine Ammar & Mahmoud Neji

  2. Laboratoire MIA, Université de La Rochelle, La Rochelle, France

    Sirine Ammar & Thierry Bouwmans

  3. SETIT (ISBS), Université de Sfax, Sfax, Tunisie

    Nizar Zaghden

Authors
  1. Sirine Ammar
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  2. Thierry Bouwmans
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  3. Nizar Zaghden
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  4. Mahmoud Neji
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Corresponding author

Correspondence to Sirine Ammar .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Ammar, S., Bouwmans, T., Zaghden, N., Neji, M. (2020). Towards an Effective Approach for Face Recognition with DCGANs Data Augmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_36

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  • DOI: https://doi.org/10.1007/978-3-030-64556-4_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64555-7

  • Online ISBN: 978-3-030-64556-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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