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An Improved GAN-Based Method for Low Resolution Face Recognition

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 715))

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Abstract

The Low Resolution Face Recognition (LR FR) issue focuses on the resolution variation problem mainly displayed in surveillance systems where faces are captured in an unconstrained environment. To address this challenging problem, we introduced a new method to recognize faces under Low and Very Low Resolution conditions. The proposed method consists of two phases : an off-line phase and an inference phase. In the off-line phase, we generate a model to transform Low-Resolution (LR) face images into High-Resolution (HR) face images via a Generative Adversarial Network (GAN). As for the inference phase, we make use of the already generated model to improve the face resolution then we extract deep features to identify the face. An experimental study was carried out on the famous ORL and FERET databases and the obtained results proved the efficiency of the proposed method to deal with Low Resolution (LR) and Very Low Resolution (VLR) Face Recognition problem.

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Author information

Authors and Affiliations

  1. MIRACL-FSEG, Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia

    Sahar Dammak

  2. MIRACL-ENET’COM, National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax, Tunisia

    Hazar Mliki

  3. MIRACL-FS, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia

    Emna Fendri

  4. Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia

    Amal Selmi

Authors
  1. Sahar Dammak
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  2. Hazar Mliki
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  3. Emna Fendri
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  4. Amal Selmi
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Corresponding author

Correspondence to Sahar Dammak .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Dammak, S., Mliki, H., Fendri, E., Selmi, A. (2023). An Improved GAN-Based Method for Low Resolution Face Recognition. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_27

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