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

18 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

Access to Document

10.1016/j.neucom.2015.11.137

Cite this

@article{68e637b9118f423d80b9092bffe242ca,

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.",

author = "Oh, {Beom Seok} and Kangrok Oh and Teoh, {Andrew Beng Jin} and Zhiping Lin and Toh, {Kar Ann}",

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: {\textcopyright} 2017 Elsevier B.V.",

year = "2017",

month = oct,

day = "25",

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

language = "English",

volume = "261",

pages = "253--265",

journal = "Review of Economic Dynamics",

issn = "1094-2025",

publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - A Gabor-based network for heterogeneous face recognition

AU - Oh, Beom Seok

AU - Oh, Kangrok

AU - Teoh, Andrew Beng Jin

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.

PY - 2017/10/25

Y1 - 2017/10/25

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85014092835&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014092835&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2015.11.137

DO - 10.1016/j.neucom.2015.11.137

M3 - Article

AN - SCOPUS:85014092835

VL - 261

SP - 253

EP - 265

JO - Review of Economic Dynamics

JF - Review of Economic Dynamics

SN - 1094-2025

ER -

A Gabor-based network for heterogeneous face recognition

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this