Face recognition using locality sparsity preserving projections

Ying Wen; Shicheng Yang; Lili Hou; Hongda Zhang

doi:10.1109/IJCNN.2016.7727662

Face recognition using locality sparsity preserving projections

Ying Wen
, Shicheng Yang
, Lili Hou
, Hongda Zhang

School of Communication and Electronic Engineering

East China Normal University

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

4 Scopus citations

Abstract

In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local structure of data. The projection of LSPP is sought such that the sparse reconstructive weights and local preserving weights can be best preserved and integrated. Extensive experiments on ORL, Yale, Yale B, AR and CMU PIE face databases show the effectiveness of the proposed LSPP.

Original language	English
Title of host publication	2016 International Joint Conference on Neural Networks, IJCNN 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3600-3607
Number of pages	8
ISBN (Electronic)	9781509006199
DOIs	https://doi.org/10.1109/IJCNN.2016.7727662
State	Published - 31 Oct 2016
Event	2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2016-October

Conference

Conference	2016 International Joint Conference on Neural Networks, IJCNN 2016
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

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10.1109/IJCNN.2016.7727662

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Wen, Y., Yang, S., Hou, L., & Zhang, H. (2016). Face recognition using locality sparsity preserving projections. In 2016 International Joint Conference on Neural Networks, IJCNN 2016 (pp. 3600-3607). Article 7727662 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2016.7727662

@inproceedings{a0d3ae82544542298bb4c5cec4284635,

title = "Face recognition using locality sparsity preserving projections",

abstract = "In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local structure of data. The projection of LSPP is sought such that the sparse reconstructive weights and local preserving weights can be best preserved and integrated. Extensive experiments on ORL, Yale, Yale B, AR and CMU PIE face databases show the effectiveness of the proposed LSPP.",

author = "Ying Wen and Shicheng Yang and Lili Hou and Hongda Zhang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

year = "2016",

month = oct,

day = "31",

doi = "10.1109/IJCNN.2016.7727662",

language = "英语",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3600--3607",

booktitle = "2016 International Joint Conference on Neural Networks, IJCNN 2016",

address = "美国",

}

Wen, Y, Yang, S, Hou, L & Zhang, H 2016, Face recognition using locality sparsity preserving projections. in 2016 International Joint Conference on Neural Networks, IJCNN 2016., 7727662, Proceedings of the International Joint Conference on Neural Networks, vol. 2016-October, Institute of Electrical and Electronics Engineers Inc., pp. 3600-3607, 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, Canada, 24/07/16. https://doi.org/10.1109/IJCNN.2016.7727662

Face recognition using locality sparsity preserving projections. / Wen, Ying; Yang, Shicheng; Hou, Lili et al.
2016 International Joint Conference on Neural Networks, IJCNN 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3600-3607 7727662 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2016-October).

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

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T1 - Face recognition using locality sparsity preserving projections

AU - Wen, Ying

AU - Yang, Shicheng

AU - Hou, Lili

AU - Zhang, Hongda

PY - 2016/10/31

Y1 - 2016/10/31

N2 - In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local structure of data. The projection of LSPP is sought such that the sparse reconstructive weights and local preserving weights can be best preserved and integrated. Extensive experiments on ORL, Yale, Yale B, AR and CMU PIE face databases show the effectiveness of the proposed LSPP.

AB - In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local structure of data. The projection of LSPP is sought such that the sparse reconstructive weights and local preserving weights can be best preserved and integrated. Extensive experiments on ORL, Yale, Yale B, AR and CMU PIE face databases show the effectiveness of the proposed LSPP.

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DO - 10.1109/IJCNN.2016.7727662

M3 - 会议稿件

AN - SCOPUS:85007188778

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 3600

EP - 3607

BT - 2016 International Joint Conference on Neural Networks, IJCNN 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 International Joint Conference on Neural Networks, IJCNN 2016

Y2 - 24 July 2016 through 29 July 2016

ER -

Face recognition using locality sparsity preserving projections

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