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A Randomly Weighted Gabor Network for Visual-Thermal Infrared Face Recognition

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Proceedings of ELM-2015 Volume 2

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 7))

Abstract

In this paper, a novel three-layer Gabor-based network is proposed for heterogeneous face recognition. The input layer of our proposed network consists of pixel-wise image patches. At the hidden layer, a set of Gabor features are extracted by a projection operation and a magnitude function. Subsequently, a non-linear activation function is utilized after weighting the extracted Gabor features with random weight vectors. Finally, the output weights are deterministically learned similarly to that in extreme learning machine. Some experimental results on private BERC visual-thermal infrared database are observed and discussed. The proposed method shows promising results based on the average test recognition accuracy.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant number: NRF-2012R1A1A2042428).

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Republic of Korea

    Beom-Seok Oh, Kangrok Oh, Andrew Beng Jin Teoh & Kar-Ann Toh

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore

    Beom-Seok Oh & Zhiping Lin

Authors
  1. Beom-Seok Oh
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  2. Kangrok Oh
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  3. Andrew Beng Jin Teoh
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  4. Zhiping Lin
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  5. Kar-Ann Toh
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Corresponding author

Correspondence to Kar-Ann Toh .

Editor information

Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

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Oh, BS., Oh, K., Teoh, A.B.J., Lin, Z., Toh, KA. (2016). A Randomly Weighted Gabor Network for Visual-Thermal Infrared Face Recognition. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_29

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  • DOI: https://doi.org/10.1007/978-3-319-28373-9_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28372-2

  • Online ISBN: 978-3-319-28373-9

  • eBook Packages: EngineeringEngineering (R0)

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