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

  • Beom-Seok Oh
  • Kangrok Oh
  • Andrew Beng Jin Teoh
  • Zhiping Lin
  • Kar-Ann Toh
Conference paper
First Online:
Part of the Proceedings in Adaptation, Learning and Optimization book series (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.

Keywords

Heterogeneous face recognition Gabor features Extreme learning machine Random weighting 
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Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Beom-Seok Oh
    • 1
    • 2
  • Kangrok Oh
    • 1
  • Andrew Beng Jin Teoh
    • 1
  • Zhiping Lin
    • 2
  • Kar-Ann Toh
    • 1
  1. 1.School of Electrical and Electronic EngineeringYonsei UniversitySeodaemun-guRepublic of Korea
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

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