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 |
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| Title of host publication | 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008 |
| Pages | 2139-2144 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2008 Sep 23 |
| 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 | Singapore |
| City | Singapore |
| Period | 08/6/3 → 08/6/5 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
Cite this
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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 - 2008/9/23
Y1 - 2008/9/23
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
ER -