dc.contributor.author |
Ajmera, Pawan K. |
|
dc.date.accessioned |
2023-03-14T05:27:21Z |
|
dc.date.available |
2023-03-14T05:27:21Z |
|
dc.date.issued |
2022-06 |
|
dc.identifier.uri |
https://link.springer.com/article/10.1007/s11277-022-09805-9 |
|
dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9681 |
|
dc.description.abstract |
Biometric systems proven to be one of the most reliable and robust method for human
identification. Integration of biometrics among the standard of living provokes the necessity
to vogue secure authentication systems. The use of palm-prints for user access and
authentication has increased in the last decade. To give the essential security and protection
benefits, conventional neural networks (CNNs) has been bestowed during this work.
The combined CNN and feature transform structure is employed for mapping palm-prints
to random base-n codes. Further, secure hash algorithm (SHA-3) is used to generate secure
palm-print templates. The proficiency of the proposed approach has been tested on PolyU,
CASIA and IIT-Delhi palm-print datasets. The best recognition performance in terms of
Equal Error Rate (EER) of 0.62% and Genuine Acceptance Rate (GAR) of 99.05% was
achieved on PolyU database. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Springer |
en_US |
dc.subject |
EEE |
en_US |
dc.subject |
Conventional neural networks |
en_US |
dc.subject |
SHA-3 |
en_US |
dc.subject |
Transformation scheme |
en_US |
dc.subject |
GAR |
en_US |
dc.title |
Upgrading Information Security and Protection for Palm-Print Templates |
en_US |
dc.type |
Article |
en_US |