Sparse random projection for efficient cancelable face feature extraction

Youngsung Kim; Kar Ann Toh

doi:10.1109/ICIEA.2008.4582897

Sparse random projection for efficient cancelable face feature extraction

Youngsung Kim, Kar Ann Toh

Department of Electrical and Electronic Engineering

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

9 Citations (Scopus)

Abstract

Based on a recently proposed framework for cancelable biometric template generation, this paper focuses on boosting the computational efficiency using a sparse random projection. Comparing with a non-sparse random projection, we show empirically that the verification accuracy of templates generated by sparse random projection do not degrade while enjoying a more efficient feature extraction process than before. This work contributes to establishment of an algorithm for effective cancelable face template generation.

Original language	English
Title of host publication	2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008
Pages	2139-2144
Number of pages	6
DOIs	https://doi.org/10.1109/ICIEA.2008.4582897
Publication status	Published - 2008
Event	2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008 - Singapore, Singapore Duration: 2008 Jun 3 → 2008 Jun 5

Publication series

Name	2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008

Other

Other	2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008
Country/Territory	Singapore
City	Singapore
Period	08/6/3 → 08/6/5

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Industrial and Manufacturing Engineering

Access to Document

10.1109/ICIEA.2008.4582897

Cite this

@inproceedings{f50ce7f6afa94cfbb531c58c0962b8f4,

title = "Sparse random projection for efficient cancelable face feature extraction",

abstract = "Based on a recently proposed framework for cancelable biometric template generation, this paper focuses on boosting the computational efficiency using a sparse random projection. Comparing with a non-sparse random projection, we show empirically that the verification accuracy of templates generated by sparse random projection do not degrade while enjoying a more efficient feature extraction process than before. This work contributes to establishment of an algorithm for effective cancelable face template generation.",

author = "Youngsung Kim and Toh, {Kar Ann}",

year = "2008",

doi = "10.1109/ICIEA.2008.4582897",

language = "English",

isbn = "9781424417186",

series = "2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008",

pages = "2139--2144",

booktitle = "2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008",

note = "2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008 ; Conference date: 03-06-2008 Through 05-06-2008",

}

Kim, Y & Toh, KA 2008, Sparse random projection for efficient cancelable face feature extraction. in 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008., 4582897, 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, pp. 2139-2144, 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, Singapore, Singapore, 08/6/3. https://doi.org/10.1109/ICIEA.2008.4582897

Sparse random projection for efficient cancelable face feature extraction. / Kim, Youngsung; Toh, Kar Ann.

2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008. 2008. p. 2139-2144 4582897 (2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008).

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

TY - GEN

T1 - Sparse random projection for efficient cancelable face feature extraction

AU - Kim, Youngsung

AU - Toh, Kar Ann

PY - 2008

Y1 - 2008

N2 - Based on a recently proposed framework for cancelable biometric template generation, this paper focuses on boosting the computational efficiency using a sparse random projection. Comparing with a non-sparse random projection, we show empirically that the verification accuracy of templates generated by sparse random projection do not degrade while enjoying a more efficient feature extraction process than before. This work contributes to establishment of an algorithm for effective cancelable face template generation.

AB - Based on a recently proposed framework for cancelable biometric template generation, this paper focuses on boosting the computational efficiency using a sparse random projection. Comparing with a non-sparse random projection, we show empirically that the verification accuracy of templates generated by sparse random projection do not degrade while enjoying a more efficient feature extraction process than before. This work contributes to establishment of an algorithm for effective cancelable face template generation.

UR - http://www.scopus.com/inward/record.url?scp=51949086167&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=51949086167&partnerID=8YFLogxK

U2 - 10.1109/ICIEA.2008.4582897

DO - 10.1109/ICIEA.2008.4582897

M3 - Conference contribution

AN - SCOPUS:51949086167

SN - 9781424417186

T3 - 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008

SP - 2139

EP - 2144

BT - 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008

T2 - 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008

Y2 - 3 June 2008 through 5 June 2008

ER -

Sparse random projection for efficient cancelable face feature extraction

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this