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Erschienen in:
Large-Scale Scene Recognition Based on Extreme Learning Machines Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

A Randomly Weighted Gabor Network for Visual-Thermal Infrared Face Recognition

verfasst von : Beom-Seok Oh, Kangrok Oh, Andrew Beng Jin Teoh, Zhiping Lin, Kar-Ann Toh

Erschienen in: Proceedings of ELM-2015 Volume 2

Verlag: Springer International Publishing

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Abstract

In this paper, a novel three-layer Gabor-based network is proposed for heterogeneous face recognition. The input layer of our proposed network consists of pixel-wise image patches. At the hidden layer, a set of Gabor features are extracted by a projection operation and a magnitude function. Subsequently, a non-linear activation function is utilized after weighting the extracted Gabor features with random weight vectors. Finally, the output weights are deterministically learned similarly to that in extreme learning machine. Some experimental results on private BERC visual-thermal infrared database are observed and discussed. The proposed method shows promising results based on the average test recognition accuracy.

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Vorheriges Kapitel Multi-instance Multi-label Learning by Extreme Learning Machine
Nächstes Kapitel Dynamic Adjustment of Hidden Layer Structure for Convex Incremental Extreme Learning Machine
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Metadaten
Titel
A Randomly Weighted Gabor Network for Visual-Thermal Infrared Face Recognition
verfasst von
Beom-Seok Oh
Kangrok Oh
Andrew Beng Jin Teoh
Zhiping Lin
Kar-Ann Toh
Copyright-Jahr
2016
Buch
Proceedings of ELM-2015 Volume 2

Print ISBN: 978-3-319-28372-2

Electronic ISBN: 978-3-319-28373-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-28373-9_29