Weighted sparse representation using a learned distance metric for face recognition

Xiaochao Qu; Suah Kim; Dessalegn Atnafu; Hyong Joong Kim

doi:10.1109/ICIP.2015.7351677

Weighted sparse representation using a learned distance metric for face recognition

Xiaochao Qu, Suah Kim, Dessalegn Atnafu, Hyong Joong Kim

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

1 Citation (Scopus)

Abstract

This paper presents a novel weighted sparse representation classification for face recognition with a learned distance metric (WSRC-LDM) which learns a Mahalanobis distance to calculate the weight and code the testing face. The Mahalanobis distance is learned by using the information-theoretic metric learning (ITML) which helps to define a better weight used in WSRC. In the meantime, the learned distance metric takes advantage of the classification rule of SRC which helps the proposed method classify more accurately. Extensive experiments verify the effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings - International Conference on Image Processing, ICIP
Publisher	IEEE Computer Society
Pages	4594-4598
Number of pages	5
Volume	2015-December
ISBN (Print)	9781479983391
DOIs	https://doi.org/10.1109/ICIP.2015.7351677
Publication status	Published - 2015 Dec 9
Event	IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: 2015 Sept 27 → 2015 Sept 30

Other

Other	IEEE International Conference on Image Processing, ICIP 2015
Country/Territory	Canada
City	Quebec City
Period	15/9/27 → 15/9/30

Keywords

Face Recognition
Metric Learning
Weighted Sparse Representation Classification

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2015.7351677

Cite this

Qu, X, Kim, S, Atnafu, D & Kim, HJ 2015, Weighted sparse representation using a learned distance metric for face recognition. in Proceedings - International Conference on Image Processing, ICIP. vol. 2015-December, 7351677, IEEE Computer Society, pp. 4594-4598, IEEE International Conference on Image Processing, ICIP 2015, Quebec City, Canada, 15/9/27. https://doi.org/10.1109/ICIP.2015.7351677

@inproceedings{f24f5dc81f7647ffa81d4c118698dec5,

title = "Weighted sparse representation using a learned distance metric for face recognition",

abstract = "This paper presents a novel weighted sparse representation classification for face recognition with a learned distance metric (WSRC-LDM) which learns a Mahalanobis distance to calculate the weight and code the testing face. The Mahalanobis distance is learned by using the information-theoretic metric learning (ITML) which helps to define a better weight used in WSRC. In the meantime, the learned distance metric takes advantage of the classification rule of SRC which helps the proposed method classify more accurately. Extensive experiments verify the effectiveness of the proposed method.",

keywords = "Face Recognition, Metric Learning, Weighted Sparse Representation Classification",

author = "Xiaochao Qu and Suah Kim and Dessalegn Atnafu and Kim, {Hyong Joong}",

year = "2015",

month = dec,

day = "9",

doi = "10.1109/ICIP.2015.7351677",

language = "English",

isbn = "9781479983391",

volume = "2015-December",

pages = "4594--4598",

booktitle = "Proceedings - International Conference on Image Processing, ICIP",

publisher = "IEEE Computer Society",

note = "IEEE International Conference on Image Processing, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",

}

TY - GEN

T1 - Weighted sparse representation using a learned distance metric for face recognition

AU - Qu, Xiaochao

AU - Kim, Suah

AU - Atnafu, Dessalegn

AU - Kim, Hyong Joong

PY - 2015/12/9

Y1 - 2015/12/9

N2 - This paper presents a novel weighted sparse representation classification for face recognition with a learned distance metric (WSRC-LDM) which learns a Mahalanobis distance to calculate the weight and code the testing face. The Mahalanobis distance is learned by using the information-theoretic metric learning (ITML) which helps to define a better weight used in WSRC. In the meantime, the learned distance metric takes advantage of the classification rule of SRC which helps the proposed method classify more accurately. Extensive experiments verify the effectiveness of the proposed method.

AB - This paper presents a novel weighted sparse representation classification for face recognition with a learned distance metric (WSRC-LDM) which learns a Mahalanobis distance to calculate the weight and code the testing face. The Mahalanobis distance is learned by using the information-theoretic metric learning (ITML) which helps to define a better weight used in WSRC. In the meantime, the learned distance metric takes advantage of the classification rule of SRC which helps the proposed method classify more accurately. Extensive experiments verify the effectiveness of the proposed method.

KW - Face Recognition

KW - Metric Learning

KW - Weighted Sparse Representation Classification

UR - http://www.scopus.com/inward/record.url?scp=84956619369&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84956619369&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2015.7351677

DO - 10.1109/ICIP.2015.7351677

M3 - Conference contribution

AN - SCOPUS:84956619369

SN - 9781479983391

VL - 2015-December

SP - 4594

EP - 4598

BT - Proceedings - International Conference on Image Processing, ICIP

PB - IEEE Computer Society

T2 - IEEE International Conference on Image Processing, ICIP 2015

Y2 - 27 September 2015 through 30 September 2015

ER -

Weighted sparse representation using a learned distance metric for face recognition

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this