Abstract
Original language | Undefined |
---|---|
Title of host publication | Advances in Biometrics |
Editors | M. Tistarelli, M. Nixon |
Place of Publication | Berlin |
Publisher | Springer |
Pages | 72-81 |
Number of pages | 10 |
ISBN (Print) | 978-3-642-01792-6 |
DOIs | |
Publication status | Published - Jun 2009 |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Publisher | Springer Verlag |
Number | 5558 |
Volume | 5558 |
Keywords
- METIS-264247
- Biometrics
- SCS-Safety
- Template Protection
- EWI-17015
- IR-68946
Cite this
}
Binary Biometric Representation through Pairwise Polar Quantization. / Chen, C.; Veldhuis, Raymond N.J.
Advances in Biometrics. ed. / M. Tistarelli; M. Nixon. Berlin : Springer, 2009. p. 72-81 10.1007/978-3-642-01793-3_8 (Lecture Notes in Computer Science; Vol. 5558, No. 5558).Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review
TY - CHAP
T1 - Binary Biometric Representation through Pairwise Polar Quantization
AU - Chen, C.
AU - Veldhuis, Raymond N.J.
N1 - 10.1007/978-3-642-01793-3_8
PY - 2009/6
Y1 - 2009/6
N2 - Binary biometric representations have great significance for data compression and template protection. In this paper, we introduce pairwise polar quantization. Furthermore, aiming to optimize the discrimination between the genuine Hamming distance (GHD) and the imposter Hamming distance (IHD), we propose two feature pairing strategies: the long-short (LS) strategy for phase quantization, as well as the long-long (LL) strategy for magnitude quantization. Experimental results for the FRGC face database and the FVC2000 fingerprint database show that phase bits provide reasonably good performance, whereas magnitude bits obtain poor performance.
AB - Binary biometric representations have great significance for data compression and template protection. In this paper, we introduce pairwise polar quantization. Furthermore, aiming to optimize the discrimination between the genuine Hamming distance (GHD) and the imposter Hamming distance (IHD), we propose two feature pairing strategies: the long-short (LS) strategy for phase quantization, as well as the long-long (LL) strategy for magnitude quantization. Experimental results for the FRGC face database and the FVC2000 fingerprint database show that phase bits provide reasonably good performance, whereas magnitude bits obtain poor performance.
KW - METIS-264247
KW - Biometrics
KW - SCS-Safety
KW - Template Protection
KW - EWI-17015
KW - IR-68946
U2 - 10.1007/978-3-642-01793-3_8
DO - 10.1007/978-3-642-01793-3_8
M3 - Chapter
SN - 978-3-642-01792-6
T3 - Lecture Notes in Computer Science
SP - 72
EP - 81
BT - Advances in Biometrics
A2 - Tistarelli, M.
A2 - Nixon, M.
PB - Springer
CY - Berlin
ER -