A Gabor-based network for heterogeneous face recognition

Beom Seok Oh, Kangrok Oh, Andrew Beng Jin Teoh, Zhiping Lin, Kar Ann Toh

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


In this paper, we propose a single hidden-layer Gabor-based network for heterogeneous face recognition. The proposed input layer contains novel computational units which propagate geometrically localized input image sub-blocks to hidden nodes. The propagated pixels are then convolved with a set of Gabor kernels followed by a randomly weighted summation and a non-linear activation function operation. The output layer adopts a linear weighting scheme which can be deterministically estimated similar to that in extreme learning machine. Our experiments on three experimental scenarios using BERC visual-thermal infrared database and CASIA visual-near infrared database show promising results for the proposed network.

Original languageEnglish
Pages (from-to)253-265
Number of pages13
Publication statusPublished - 2017 Oct 25

Bibliographical note

Funding Information:
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 numbers: NRF-2012R1A1A2042428 and NRF-2015R1D1A1A09061316 ).

Publisher Copyright:
© 2017 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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