Pin-Code Authentication by Local Proximity Based Touchstroke Classifier
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017069" target="_blank" >RIV/62690094:18450/20:50017069 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-45385-5_31" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-45385-5_31</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-45385-5_31" target="_blank" >10.1007/978-3-030-45385-5_31</a>
Alternative languages
Result language
angličtina
Original language name
Pin-Code Authentication by Local Proximity Based Touchstroke Classifier
Original language description
Most of the recently released touchscreen devices enable fingerprint authentication; while pin-codes connected to the fingerprints are still in common usage. Among various biometric feature extractors, such as time-based, frequency-based or even pressure-based, the most reliable and implementable with mathematical infrastructure is location-based approach for enhancing security of touchscreen devices. Therefore, in this paper, we propose fundamentals of a novel feature extraction protocol to strengthen pin-codes by calculating local proximity of the touches on the screen without long training sessions. We presented a fuzzy-like area methodology for finding outputs for each input and also conducted experiments to show the discretization of the outputs per real and fraud attempts. © Springer Nature Switzerland AG 2020.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-030-45384-8
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
350-361
Publisher name
Springer
Place of publication
Cham
Event location
Granada, Spain
Event date
May 6, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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