dc.contributor.author |
Brown, Dane
|
|
dc.contributor.author |
Bradshaw, K
|
|
dc.date.accessioned |
2017-06-07T06:25:28Z |
|
dc.date.available |
2017-06-07T06:25:28Z |
|
dc.date.issued |
2016-09 |
|
dc.identifier.citation |
Brown, D. and Bradshaw, K. 2016. A dynamically weighted multi-modal biometric security system. Southern Africa Telecommunication Networks and Applications Conference (SATNAC), 4-7 September 2016, George, p. 254-258 |
en_US |
dc.identifier.isbn |
978-0-620-72418-0 |
|
dc.identifier.uri |
http://www.satnac.org.za/proceedings/2016/SATNAC%202016%20Proceedings%20Final.pdf
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/9118
|
|
dc.description |
Southern Africa Telecommunication Networks and Applications Conference, 4-7 September 2016, George |
en_US |
dc.description.abstract |
The face, fingerprint and palmprint feature vectors are automatically extracted and dynamically selected for fusion at the feature-level, toward an improved human identification accuracy. The feature-level has a higher potential accuracy than the match score-level. However, leveraging this potential requires a new approach. This work demonstrates a novel dynamic weighting algorithm for improved image-based biometric feature-fusion. A comparison is performed on uni-modal, bi-modal, tri-modal and proposed dynamic approaches. The proposed dynamic approach yields a high genuine acceptance rate of 99.25% genuine acceptance rate at a false acceptance rate of 1% on challenging datasets and big impostor datasets. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
www.satnac.org.za |
en_US |
dc.relation.ispartofseries |
Worklist;18211 |
|
dc.subject |
Face feature vectors |
en_US |
dc.subject |
Fingerprint feature vectors |
en_US |
dc.subject |
Palmprint feature vectors |
en_US |
dc.subject |
Biometric security systems |
en_US |
dc.title |
A dynamically weighted multi-modal biometric security system |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Brown, D., & Bradshaw, K. (2016). A dynamically weighted multi-modal biometric security system. www.satnac.org.za. http://hdl.handle.net/10204/9118 |
en_ZA |
dc.identifier.chicagocitation |
Brown, Dane, and K Bradshaw. "A dynamically weighted multi-modal biometric security system." (2016): http://hdl.handle.net/10204/9118 |
en_ZA |
dc.identifier.vancouvercitation |
Brown D, Bradshaw K, A dynamically weighted multi-modal biometric security system; www.satnac.org.za; 2016. http://hdl.handle.net/10204/9118 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Brown, Dane
AU - Bradshaw, K
AB - The face, fingerprint and palmprint feature vectors are automatically extracted and dynamically selected for fusion at the feature-level, toward an improved human identification accuracy. The feature-level has a higher potential accuracy than the match score-level. However, leveraging this potential requires a new approach. This work demonstrates a novel dynamic weighting algorithm for improved image-based biometric feature-fusion. A comparison is performed on uni-modal, bi-modal, tri-modal and proposed dynamic approaches. The proposed dynamic approach yields a high genuine acceptance rate of 99.25% genuine acceptance rate at a false acceptance rate of 1% on challenging datasets and big impostor datasets.
DA - 2016-09
DB - ResearchSpace
DP - CSIR
KW - Face feature vectors
KW - Fingerprint feature vectors
KW - Palmprint feature vectors
KW - Biometric security systems
LK - https://researchspace.csir.co.za
PY - 2016
SM - 978-0-620-72418-0
T1 - A dynamically weighted multi-modal biometric security system
TI - A dynamically weighted multi-modal biometric security system
UR - http://hdl.handle.net/10204/9118
ER -
|
en_ZA |