A Gabor-based network for heterogeneous face recognition

被引：19

作者：

Oh, Beom-Seok ^{[2
]}

Oh, Kangrok ^{[1
]}

Teoh, Andrew Beng Jin ^{[1
]}

Lin, Zhiping ^{[2
]}

Toh, Kar-Ann ^{[1
]}

机构：

[1] Yonsei Univ, Sch Elect & Elect Engn, 50 Yonsei Ro, Seoul 03722, South Korea

[2] Nanyang Technol Univ, Sch Elect & Elect Engn, 50 Nanyang Ave, Singapore 639798, Singapore

来源：

NEUROCOMPUTING | 2017年 / 261卷

基金：

新加坡国家研究基金会;

关键词：

Heterogeneous face recognition; Gabor features; Extreme learning machine; Random weighting; EXTREME LEARNING-MACHINE; SPECTRAL REGRESSION;

D O I：

10.1016/j.neucom.2015.11.137

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：253 / 265

页数：13

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