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A Survey on Face and Body Based Human Recognition Robust to Image Blurring and Low Illumination

Author

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  • Ja Hyung Koo

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Se Woon Cho

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Na Rae Baek

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Young Won Lee

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

  • Kang Ryoung Park

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea)

Abstract

Many studies have been actively conducted on human recognition in indoor and outdoor environments. This is because human recognition methods in such environments are closely related to everyday life situations. Besides, these methods can be applied for finding missing children and identifying criminals. Methods for human recognition in indoor and outdoor environments can be classified into three categories: face-, body-, and gait-based methods. There are various factors that hinder indoor and outdoor human recognition, for example, blurring of captured images, cutoff in images due to the camera angle, and poor recognition in images acquired in low-illumination environments. Previous studies conducted to solve these problems focused on facial recognition only. This is because the face is typically assumed to contain more important information for human recognition than the body. However, when a human face captured by a distant camera is small, or even impossible to identify with the naked eye, the body’s information can help with recognition. For this reason, this survey paper reviews both face- and body-based human recognition methods. In previous surveys, recognition on low-resolution images were reviewed. However, survey papers on blurred images are not comprehensive. Therefore, in this paper, we review studies on blurred image restoration in detail by classifying them based on whether deep learning was used and whether the human face and body were combined. Although previous survey papers on recognition covered low-illumination environments as well, they excluded deep learning methods. Therefore, in this survey, we also include details on deep-learning-based low-illumination image recognition methods. We aim to help researchers who will study related fields in the future.

Suggested Citation

  • Ja Hyung Koo & Se Woon Cho & Na Rae Baek & Young Won Lee & Kang Ryoung Park, 2022. "A Survey on Face and Body Based Human Recognition Robust to Image Blurring and Low Illumination," Mathematics, MDPI, vol. 10(9), pages 1-15, May.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1522-:d:807574
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    References listed on IDEAS

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    1. Ja Hyung Koo & Se Woon Cho & Na Rae Baek & Kang Ryoung Park, 2021. "Multimodal Human Recognition in Significantly Low Illumination Environment Using Modified EnlightenGAN," Mathematics, MDPI, vol. 9(16), pages 1-43, August.
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    Cited by:

    1. Hao-Qi Xu & Cong Hu & He-Feng Yin, 2022. "Enhancing the Transferability of Adversarial Examples with Feature Transformation," Mathematics, MDPI, vol. 10(16), pages 1-14, August.
    2. Damjan Vlaj & Andrej Zgank, 2022. "Acoustic Gender and Age Classification as an Aid to Human–Computer Interaction in a Smart Home Environment," Mathematics, MDPI, vol. 11(1), pages 1-22, December.

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