A Gabor-based network for heterogeneous face recognition

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

doi:10.1016/j.neucom.2015.11.137

A Gabor-based network for heterogeneous face recognition

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

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

16 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 language	English
Pages (from-to)	253-265
Number of pages	13
Journal	Neurocomputing
Volume	261
DOIs	https://doi.org/10.1016/j.neucom.2015.11.137
Publication status	Published - 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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10.1016/j.neucom.2015.11.137

Cite this

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title = "A Gabor-based network for heterogeneous face recognition",

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.",

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doi = "10.1016/j.neucom.2015.11.137",

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AU - Lin, Zhiping

AU - Toh, Kar Ann

N1 - 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.

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A Gabor-based network for heterogeneous face recognition

Abstract

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