Coupled discriminant multi-manifold analysis with application to low-resolution face recognition

Junjun Jiang; Ruimin Hu; Zhen Han; Liang Chen; Jun Chen

doi:10.1007/978-3-319-14445-0_4

Coupled discriminant multi-manifold analysis with application to low-resolution face recognition

Junjun Jiang^*
, Ruimin Hu
, Zhen Han
, Liang Chen
, Jun Chen

^*Corresponding author for this work

Wuhan University

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

9 Scopus citations

Abstract

The problem of matching a low-resolution (LR) face image to a gallery of high-resolution (HR) face images is addressed in this letter. Previous research has focused on introducing a learning based superresolution (LBSR) method before matching or transforming LR and HR faces into a unified feature space (UFS) for matching. To identify LR faces, we present a method called coupled discriminant multi-manifold analysis (CDMMA). In CDMMA, we first explore the neighborhood information as well as local geometric structure of the multi-manifold space spanned by the samples. And then, we explicitly learn two mappings to project LR and HR faces to a unified discriminative feature space (UDFS) through a supervised manner, where the discriminative information is maximized for classification. After that, the conventional classification method is applied in the CDMMA for final identification. Experimental results conducted on two standard face recognition databases demonstrate the superiority of the proposed CDMMA.

Original language	English
Title of host publication	MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings
Editors	Xiangjian He, Dacheng Tao, Muhammad Abul Hasan, Suhuai Luo, Changsheng Xu, Jie Yang
Publisher	Springer Verlag
Pages	37-48
Number of pages	12
ISBN (Electronic)	9783319144443
DOIs	https://doi.org/10.1007/978-3-319-14445-0_4
State	Published - 2015
Externally published	Yes
Event	21st International Conference on MultiMedia Modeling, MMM 2015 - Sydney, Australia Duration: 5 Jan 2015 → 7 Jan 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8935
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on MultiMedia Modeling, MMM 2015
Country/Territory	Australia
City	Sydney
Period	5/01/15 → 7/01/15

Keywords

Discriminant analysis
Face recognition
Low-resolution
Multi-manifold
Super-resolution

Access to Document

10.1007/978-3-319-14445-0_4

Cite this

Jiang, J., Hu, R., Han, Z., Chen, L., & Chen, J. (2015). Coupled discriminant multi-manifold analysis with application to low-resolution face recognition. In X. He, D. Tao, M. A. Hasan, S. Luo, C. Xu, & J. Yang (Eds.), MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings (pp. 37-48). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8935). Springer Verlag. https://doi.org/10.1007/978-3-319-14445-0_4

Jiang, Junjun ; Hu, Ruimin ; Han, Zhen et al. / Coupled discriminant multi-manifold analysis with application to low-resolution face recognition. MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings. editor / Xiangjian He ; Dacheng Tao ; Muhammad Abul Hasan ; Suhuai Luo ; Changsheng Xu ; Jie Yang. Springer Verlag, 2015. pp. 37-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{42cc04e0a45f4423abe64019ecc4fe0b,

title = "Coupled discriminant multi-manifold analysis with application to low-resolution face recognition",

abstract = "The problem of matching a low-resolution (LR) face image to a gallery of high-resolution (HR) face images is addressed in this letter. Previous research has focused on introducing a learning based superresolution (LBSR) method before matching or transforming LR and HR faces into a unified feature space (UFS) for matching. To identify LR faces, we present a method called coupled discriminant multi-manifold analysis (CDMMA). In CDMMA, we first explore the neighborhood information as well as local geometric structure of the multi-manifold space spanned by the samples. And then, we explicitly learn two mappings to project LR and HR faces to a unified discriminative feature space (UDFS) through a supervised manner, where the discriminative information is maximized for classification. After that, the conventional classification method is applied in the CDMMA for final identification. Experimental results conducted on two standard face recognition databases demonstrate the superiority of the proposed CDMMA.",

keywords = "Discriminant analysis, Face recognition, Low-resolution, Multi-manifold, Super-resolution",

author = "Junjun Jiang and Ruimin Hu and Zhen Han and Liang Chen and Jun Chen",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 21st International Conference on MultiMedia Modeling, MMM 2015 ; Conference date: 05-01-2015 Through 07-01-2015",

year = "2015",

doi = "10.1007/978-3-319-14445-0\_4",

language = "英语",

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

publisher = "Springer Verlag",

pages = "37--48",

editor = "Xiangjian He and Dacheng Tao and Hasan, \{Muhammad Abul\} and Suhuai Luo and Changsheng Xu and Jie Yang",

booktitle = "MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings",

address = "德国",

}

Jiang, J, Hu, R, Han, Z, Chen, L & Chen, J 2015, Coupled discriminant multi-manifold analysis with application to low-resolution face recognition. in X He, D Tao, MA Hasan, S Luo, C Xu & J Yang (eds), MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8935, Springer Verlag, pp. 37-48, 21st International Conference on MultiMedia Modeling, MMM 2015, Sydney, Australia, 5/01/15. https://doi.org/10.1007/978-3-319-14445-0_4

Coupled discriminant multi-manifold analysis with application to low-resolution face recognition. / Jiang, Junjun; Hu, Ruimin; Han, Zhen et al.
MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings. ed. / Xiangjian He; Dacheng Tao; Muhammad Abul Hasan; Suhuai Luo; Changsheng Xu; Jie Yang. Springer Verlag, 2015. p. 37-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8935).

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

TY - GEN

T1 - Coupled discriminant multi-manifold analysis with application to low-resolution face recognition

AU - Jiang, Junjun

AU - Hu, Ruimin

AU - Han, Zhen

AU - Chen, Liang

AU - Chen, Jun

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - The problem of matching a low-resolution (LR) face image to a gallery of high-resolution (HR) face images is addressed in this letter. Previous research has focused on introducing a learning based superresolution (LBSR) method before matching or transforming LR and HR faces into a unified feature space (UFS) for matching. To identify LR faces, we present a method called coupled discriminant multi-manifold analysis (CDMMA). In CDMMA, we first explore the neighborhood information as well as local geometric structure of the multi-manifold space spanned by the samples. And then, we explicitly learn two mappings to project LR and HR faces to a unified discriminative feature space (UDFS) through a supervised manner, where the discriminative information is maximized for classification. After that, the conventional classification method is applied in the CDMMA for final identification. Experimental results conducted on two standard face recognition databases demonstrate the superiority of the proposed CDMMA.

AB - The problem of matching a low-resolution (LR) face image to a gallery of high-resolution (HR) face images is addressed in this letter. Previous research has focused on introducing a learning based superresolution (LBSR) method before matching or transforming LR and HR faces into a unified feature space (UFS) for matching. To identify LR faces, we present a method called coupled discriminant multi-manifold analysis (CDMMA). In CDMMA, we first explore the neighborhood information as well as local geometric structure of the multi-manifold space spanned by the samples. And then, we explicitly learn two mappings to project LR and HR faces to a unified discriminative feature space (UDFS) through a supervised manner, where the discriminative information is maximized for classification. After that, the conventional classification method is applied in the CDMMA for final identification. Experimental results conducted on two standard face recognition databases demonstrate the superiority of the proposed CDMMA.

KW - Discriminant analysis

KW - Face recognition

KW - Low-resolution

KW - Multi-manifold

KW - Super-resolution

UR - https://www.scopus.com/pages/publications/84919701700

U2 - 10.1007/978-3-319-14445-0_4

DO - 10.1007/978-3-319-14445-0_4

M3 - 会议稿件

AN - SCOPUS:84919701700

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

SP - 37

EP - 48

BT - MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings

A2 - He, Xiangjian

A2 - Tao, Dacheng

A2 - Hasan, Muhammad Abul

A2 - Luo, Suhuai

A2 - Xu, Changsheng

A2 - Yang, Jie

PB - Springer Verlag

T2 - 21st International Conference on MultiMedia Modeling, MMM 2015

Y2 - 5 January 2015 through 7 January 2015

ER -

Jiang J, Hu R, Han Z, Chen L, Chen J. Coupled discriminant multi-manifold analysis with application to low-resolution face recognition. In He X, Tao D, Hasan MA, Luo S, Xu C, Yang J, editors, MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings. Springer Verlag. 2015. p. 37-48. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-14445-0_4

Coupled discriminant multi-manifold analysis with application to low-resolution face recognition

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Keywords

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