Frequency and time localization in biometrics: STFT vs. CWT

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F18%3A50014704" target="_blank" >RIV/62690094:18450/18:50014704 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-319-92058-0_69" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-92058-0_69</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-92058-0_69" target="_blank" >10.1007/978-3-319-92058-0_69</a>

Result language

angličtina

Original language name

Frequency and time localization in biometrics: STFT vs. CWT

Original language description

Biometrics is a science discipline dealing with unique traits of the individuals including habitual characteristics. Once the unique features revealed from these characteristics are extracted as signals instead of raw data, it is possible to search for the new distinguishable traits. One of the traits, if any kind of time based signal is extracted, could be frequency component versus time component of the signal, depending on the content of the feature extraction and yet most of the signals consisting of a magnitude or a value as a dependent variable fit the requirements. The magnitude/value representation may vary indeed; however our previous researches proved that key-codes and key-press signals in biometric keystroke authentication and dislocation and speed signals in online signature verification are very useful of this kind of analysis. Therefore in this paper, we present the methods for extracting distinguishable features and frequency vs. time representation by short time Fourier transform (STFT) and continuous wavelet transformation (CWT) to introduce related research methodologies by comparing the outcomes and future opportunities. While presenting our approach to Biometric keystroke authentication and online signature verification, we also aim to present the differences of these methods with basic roadmaps and graphical results.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture notes in computer science

ISBN

978-3-319-92057-3

ISSN

0302-9743

e-ISSN

neuvedeno

Number of pages

7

Pages from-to

722-728

Publisher name

Springer

Place of publication

Cham

Event location

Montreal

Event date

Jun 25, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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