Face recognition with multi-scale block local ternary patterns

Lian Zhu; Yan Zhang; Changyin Sun; Wankou Yang

doi:10.1007/978-3-642-36669-7_27

Face recognition with multi-scale block local ternary patterns

Lian Zhu
, Yan Zhang
, Changyin Sun
, Wankou Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this paper, we propose a novel approach to face recognition, called Multi-scale Block Local Ternary Patterns (MB-LTP), which considers both local and various scale texture information to represent face images. In MB-LTP, we compare average values of sub-regions and use a 3-valued codes method to get the MB-LTP value. The MB-LTP histograms are then extracted and concatenated into a single, spatially enhanced feature vector representing the face image in recognition. We use a nearest neighbor classifier in the computed feature space with Chi square as a dissimilarity measure. MB-LTP code presents several advantages: (1)It is more robust than LBP;(2)it is more discriminative and less sensitive to noise;(3)it encodes not only microstructures but also macrostructures of image patterns. Experiments on ORL and AR databases show that the proposed MB-LTP method significantly outperforms other LBP based face recognition algorithms.

Original language	English
Title of host publication	Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers
Pages	216-222
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-642-36669-7_27
State	Published - 2013
Externally published	Yes
Event	3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012 - Nanjing, China Duration: 15 Oct 2012 → 17 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7751 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012
Country/Territory	China
City	Nanjing
Period	15/10/12 → 17/10/12

Keywords

Face recognition
LBP
MB-LTP

Access to Document

10.1007/978-3-642-36669-7_27

Cite this

Zhu, L., Zhang, Y., Sun, C., & Yang, W. (2013). Face recognition with multi-scale block local ternary patterns. In Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers (pp. 216-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7751 LNCS). https://doi.org/10.1007/978-3-642-36669-7_27

Zhu, Lian ; Zhang, Yan ; Sun, Changyin et al. / Face recognition with multi-scale block local ternary patterns. Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. pp. 216-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{39f5ef6bb9ae496e9912c0615588f09f,

title = "Face recognition with multi-scale block local ternary patterns",

abstract = "In this paper, we propose a novel approach to face recognition, called Multi-scale Block Local Ternary Patterns (MB-LTP), which considers both local and various scale texture information to represent face images. In MB-LTP, we compare average values of sub-regions and use a 3-valued codes method to get the MB-LTP value. The MB-LTP histograms are then extracted and concatenated into a single, spatially enhanced feature vector representing the face image in recognition. We use a nearest neighbor classifier in the computed feature space with Chi square as a dissimilarity measure. MB-LTP code presents several advantages: (1)It is more robust than LBP;(2)it is more discriminative and less sensitive to noise;(3)it encodes not only microstructures but also macrostructures of image patterns. Experiments on ORL and AR databases show that the proposed MB-LTP method significantly outperforms other LBP based face recognition algorithms.",

keywords = "Face recognition, LBP, MB-LTP",

author = "Lian Zhu and Yan Zhang and Changyin Sun and Wankou Yang",

year = "2013",

doi = "10.1007/978-3-642-36669-7\_27",

language = "英语",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "216--222",

booktitle = "Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers",

note = "3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012 ; Conference date: 15-10-2012 Through 17-10-2012",

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Zhu, L, Zhang, Y, Sun, C & Yang, W 2013, Face recognition with multi-scale block local ternary patterns. in Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7751 LNCS, pp. 216-222, 3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012, Nanjing, China, 15/10/12. https://doi.org/10.1007/978-3-642-36669-7_27

Face recognition with multi-scale block local ternary patterns. / Zhu, Lian; Zhang, Yan; Sun, Changyin et al.
Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. p. 216-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7751 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Face recognition with multi-scale block local ternary patterns

AU - Zhu, Lian

AU - Zhang, Yan

AU - Sun, Changyin

AU - Yang, Wankou

PY - 2013

Y1 - 2013

N2 - In this paper, we propose a novel approach to face recognition, called Multi-scale Block Local Ternary Patterns (MB-LTP), which considers both local and various scale texture information to represent face images. In MB-LTP, we compare average values of sub-regions and use a 3-valued codes method to get the MB-LTP value. The MB-LTP histograms are then extracted and concatenated into a single, spatially enhanced feature vector representing the face image in recognition. We use a nearest neighbor classifier in the computed feature space with Chi square as a dissimilarity measure. MB-LTP code presents several advantages: (1)It is more robust than LBP;(2)it is more discriminative and less sensitive to noise;(3)it encodes not only microstructures but also macrostructures of image patterns. Experiments on ORL and AR databases show that the proposed MB-LTP method significantly outperforms other LBP based face recognition algorithms.

AB - In this paper, we propose a novel approach to face recognition, called Multi-scale Block Local Ternary Patterns (MB-LTP), which considers both local and various scale texture information to represent face images. In MB-LTP, we compare average values of sub-regions and use a 3-valued codes method to get the MB-LTP value. The MB-LTP histograms are then extracted and concatenated into a single, spatially enhanced feature vector representing the face image in recognition. We use a nearest neighbor classifier in the computed feature space with Chi square as a dissimilarity measure. MB-LTP code presents several advantages: (1)It is more robust than LBP;(2)it is more discriminative and less sensitive to noise;(3)it encodes not only microstructures but also macrostructures of image patterns. Experiments on ORL and AR databases show that the proposed MB-LTP method significantly outperforms other LBP based face recognition algorithms.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers

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ER -

Zhu L, Zhang Y, Sun C, Yang W. Face recognition with multi-scale block local ternary patterns. In Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. p. 216-222. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-36669-7_27

Face recognition with multi-scale block local ternary patterns

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