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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 31, Number 9(1) (2019)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 2771-2776
S&M1971 Letter of Special Issue https://doi.org/10.18494/SAM.2019.2348 Published: September 9, 2019 High-performance Two-stage Face Recognition Algorithm for Reduced Illumination Effect [PDF] Cheng-Yu Yeh, Chien-Fu Chen, and Chun-Cheng Lin (Received February 25, 2019; Accepted July 9, 2019) Keywords: face recognition, local binary pattern (LBP), LBP histogram (LBPH), Fisherface, principal component analysis (PCA), linear discriminant analysis (LDA)
Face recognition, which is a method of identifying a person in a digital image, is widely applied to biometric-based authentication systems. A significant decrease in recognition rate is caused by extracted features that are affected by illumination. In an attempt to resolve this problem, in this paper, we present a two-stage algorithm, namely, a local binary pattern (LBP) followed by the algorithm Fisherface. As the first step of this work, a face image is converted to an LBP, which is then projected onto a low-dimensional feature space using Fisherface for subsequent classification and recognition. As a result, the outperformance of this work is demonstrated by a recognition rate of up to 96.45%, a figure far beyond 67.97% using the LBP histogram (LBPH), 84.69% using Fisherface, and 93.09% using the support vector machine (SVM) algorithm.
Corresponding author: Chun-Cheng Lin  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Cheng-Yu Yeh, Chien-Fu Chen, and Chun-Cheng Lin, High-performance Two-stage Face Recognition Algorithm for Reduced Illumination Effect, Sens. Mater., Vol. 31, No. 9, 2019, p. 2771-2776. |
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