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 journalArticle

7 Citations (Scopus)

Abstract

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
JournalNeurocomputing
Volume261
DOIs
Publication statusPublished - 2017 Oct 25

Fingerprint

Face recognition
Databases
Infrared radiation
Learning systems
Hot Temperature
Pixels
Chemical activation
Experiments
Facial Recognition
Data base
Infrared
Machine Learning
Node
Experiment
Activation
Weighting
Machine learning
Kernel
Scenarios

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

Cite this

Oh, Beom Seok ; Oh, Kangrok ; Teoh, Andrew Beng Jin ; Lin, Zhiping ; Toh, Kar Ann. / A Gabor-based network for heterogeneous face recognition. In: Neurocomputing. 2017 ; Vol. 261. pp. 253-265.
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A Gabor-based network for heterogeneous face recognition. / Oh, Beom Seok; Oh, Kangrok; Teoh, Andrew Beng Jin; Lin, Zhiping; Toh, Kar Ann.

In: Neurocomputing, Vol. 261, 25.10.2017, p. 253-265.

Research output: Contribution to journalArticle

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