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Hyperspectral face recognition with histogram of oriented gradient features and collaborative representation-based classifier

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Abstract

Hyperspectral face recognition plays an important role in remote sensing. However, it faces many challenges such as difficulty in data acquisition, low signal to noise ratio (SNR), and high dimensionality. In this paper, we develop a novel method for hyperspectral face recognition by extracting histogram of oriented features (HOG) and using collaborate representation-based classifier (CRC) to classify unknown face data cubes. To improve overall classification rates, we also implement noise reduction in hyperspectral face data cubes. We also crop the face images by a bounding box and use this bounding box image to classify the testing faces. Experiments show that our new method outperforms several existing methods for both the PolyU-HSFD dataset and the CMU-HSFD dataset for hyperspectral face recognition. The contribution of this paper is the following: We introduce the MNF-based denoising method in this paper, which is new to the best of our knowledge. We also combine it with HOG features and CRC classifier so that better recognition rate can be achieved.

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Acknowledgements

We thank David Zhang, Lei Zhang, and Meng Yang for their PolyU-HSFD dataset and their source code for the CRC method. We also thank Pan et al. for their CMU-HSFD dataset and Masayuki Tanka for the face part detection Matlab source code.

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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

    Wen Fang Xie

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  1. Guang Yi Chen
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  2. Adam Krzyzak
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  3. Wen Fang Xie
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Correspondence to Guang Yi Chen.

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Chen, G.Y., Krzyzak, A. & Xie, W.F. Hyperspectral face recognition with histogram of oriented gradient features and collaborative representation-based classifier. Multimed Tools Appl 81, 2299–2310 (2022). https://doi.org/10.1007/s11042-021-11691-5

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  • DOI: https://doi.org/10.1007/s11042-021-11691-5

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