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Hyperspectral Face Recognition via Existing 2D Face Recognition Methods

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Advanced Intelligent Computing Technology and Applications (ICIC 2024)

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Abstract

Hyperspectral imaging provides new chances for face recognition by improved discrimination in the spectral dimension. In this paper, we investigate hyperspectral face recognition by studying twenty-four existing 2D face recognition methods in combination with collaborate representation-based classifier (CRC). Since hyperspectral face data cubes contain significant amount of noise, we perform denoising on hyperspectral face data cubes by means of minimum noise fraction and video block matching 3D filtering. We crop the face images by a bounding box and use this bounding box image to classify the testing faces. We also conduct voting from the CRC output to decide the label of the unknown face cubes. Experiments show that histogram of oriented gradients achieves 100% correct classification rate for the CMU-HSFD dataset and local binary patterns obtain 96.8% correct classification rate for the PolyU-HSFD dataset.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Guang Yi Chen & Adam Krzyzak

  2. Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Wenfang Xie

Authors
  1. Guang Yi Chen
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  2. Wenfang Xie
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  3. Adam Krzyzak
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Corresponding author

Correspondence to Guang Yi Chen .

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Editors and Affiliations

  1. Eastern Institute of Technology, Ningbo, China

    De-Shuang Huang

  2. Tianjin University of Science and Technology, Tianjin, China

    Xiankun Zhang

  3. Xiamen University, Xiamen, China

    Jiayang Guo

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, G.Y., Xie, W., Krzyzak, A. (2024). Hyperspectral Face Recognition via Existing 2D Face Recognition Methods. In: Huang, DS., Zhang, X., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14866. Springer, Singapore. https://doi.org/10.1007/978-981-97-5594-3_2

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  • DOI: https://doi.org/10.1007/978-981-97-5594-3_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-5593-6

  • Online ISBN: 978-981-97-5594-3

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