dc.contributor.author |
Darlow, Luke Nicholas
|
|
dc.contributor.author |
Rosman, Benjamin S
|
|
dc.date.accessioned |
2017-09-29T06:47:40Z |
|
dc.date.available |
2017-09-29T06:47:40Z |
|
dc.date.issued |
2017-10 |
|
dc.identifier.citation |
Darlow, L.N. and Rosman, B.S. 2017. Fingerprint Minutiae Extraction using Deep Learning. International Joint Conference on Biometrics, 1-4 October 2017, Denver, Colorado, USA |
en_US |
dc.identifier.uri |
http://www.ijcb2017.org/ijcb2017/program.php
|
|
dc.identifier.uri |
https://www.benjaminrosman.com/papers.html
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/9618
|
|
dc.description |
International Joint Conference on Biometrics, 1-4 October 2017, Denver, Colorado, USA |
en_US |
dc.description.abstract |
The high variability of fingerprint data (owing to, e.g., differences in quality, moisture conditions, and scanners) makes the task of minutiae extraction challenging, particularly when approached from a stance that relies on tunable algorithmic components, such as image enhancement. We pose minutiae extraction as a machine learning problem and propose a deep neural network – MENet, for Minutiae Extraction Network – to learn a data-driven representation of minutiae points. By using the existing capabilities of several minutiae extraction algorithms, we establish a voting scheme to construct training data, and so train MENet in an automated fashion on a large dataset for robustness and portability, thus eliminating the need for tedious manual data labelling. We present a post-processing procedure that determines precise minutiae locations from the output of MENet. We show that MENet performs favourably in comparisons against existing minutiae extractors. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Worklist;19466 |
|
dc.subject |
Fingerprint detection |
en_US |
dc.subject |
Minutiae detection |
en_US |
dc.subject |
Deep learning |
en_US |
dc.title |
Fingerprint Minutiae Extraction using Deep Learning |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Darlow, L. N., & Rosman, B. S. (2017). Fingerprint Minutiae Extraction using Deep Learning. http://hdl.handle.net/10204/9618 |
en_ZA |
dc.identifier.chicagocitation |
Darlow, Luke Nicholas, and Benjamin S Rosman. "Fingerprint Minutiae Extraction using Deep Learning." (2017): http://hdl.handle.net/10204/9618 |
en_ZA |
dc.identifier.vancouvercitation |
Darlow LN, Rosman BS, Fingerprint Minutiae Extraction using Deep Learning; 2017. http://hdl.handle.net/10204/9618 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Darlow, Luke Nicholas
AU - Rosman, Benjamin S
AB - The high variability of fingerprint data (owing to, e.g., differences in quality, moisture conditions, and scanners) makes the task of minutiae extraction challenging, particularly when approached from a stance that relies on tunable algorithmic components, such as image enhancement. We pose minutiae extraction as a machine learning problem and propose a deep neural network – MENet, for Minutiae Extraction Network – to learn a data-driven representation of minutiae points. By using the existing capabilities of several minutiae extraction algorithms, we establish a voting scheme to construct training data, and so train MENet in an automated fashion on a large dataset for robustness and portability, thus eliminating the need for tedious manual data labelling. We present a post-processing procedure that determines precise minutiae locations from the output of MENet. We show that MENet performs favourably in comparisons against existing minutiae extractors.
DA - 2017-10
DB - ResearchSpace
DP - CSIR
KW - Fingerprint detection
KW - Minutiae detection
KW - Deep learning
LK - https://researchspace.csir.co.za
PY - 2017
T1 - Fingerprint Minutiae Extraction using Deep Learning
TI - Fingerprint Minutiae Extraction using Deep Learning
UR - http://hdl.handle.net/10204/9618
ER -
|
en_ZA |