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Multi-Modal User Authentication Usin...

Cheung, William.

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Multi-Modal User Authentication Using Biometrics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-Modal User Authentication Using Biometrics./
Author:	Cheung, William.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	62 p.
Notes:	Source: Masters Abstracts International, Volume: 82-09.
Contained By:	Masters Abstracts International82-09.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28315954
ISBN:	9798582515913

Multi-Modal User Authentication Using Biometrics.
Cheung, William.

Multi-Modal User Authentication Using Biometrics. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 62 p.

Source: Masters Abstracts International, Volume: 82-09.

Thesis (M.S.)--Fordham University, 2021.

This item must not be sold to any third party vendors.

The connectivity of smart technology is ever increasing with the expansion of internet availability. Emerging applications such as financial transactions, healthcare check-ups, and property access can be made through smart technologies. This also presents a new vulnerability as hackers have more opportunities to attack users. Therefore, there is an immediate emphasis on a strong authentication system. While passwords, PINs, or pattern locks can overwhelm users, active biometric schemes like retina scans require active use and cannot be used in continuous situations. A solution to this is the use of implicit continuous biometrics such as heart rate, gait, and breathing patterns. In this work, we present two context-dependent soft-biometric-based wearable authentication system strategies utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that in a sedentary state, a binary support vector machine with radial basis function (RBF) kernel can achieve an average accuracy of $0.94 \\pm 0.07$ and $F_1$ score of $0.93 \\pm 0.08$. In a non-sedentary state, $k$- Nearest Neighbors ($k$=2) can achieve an average accuracy of $0.93 \\pm 0.06$ and $F_1$ score of $0.93 \\pm 0.03$, which shows the promise of this work. Considering the availability of a single user's data, we develop unary models and obtain an average accuracy of $0.72 \\pm 0.10$ and a $F_1$ score $0.73 \\pm 0.06$ when sedentary and $0.72 \\pm 0.10$ and a $F_1$ score $0.72 \\pm 0.09$ respective scores when non-stationary.

ISBN: 9798582515913Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Implicit Authentication

Multi-Modal User Authentication Using Biometrics.
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245 1 0 $a Multi-Modal User Authentication Using Biometrics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 62 p.
500 $a Source: Masters Abstracts International, Volume: 82-09.
500 $a Advisor: Vhaduri, Sudip.
502 $a Thesis (M.S.)--Fordham University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The connectivity of smart technology is ever increasing with the expansion of internet availability. Emerging applications such as financial transactions, healthcare check-ups, and property access can be made through smart technologies. This also presents a new vulnerability as hackers have more opportunities to attack users. Therefore, there is an immediate emphasis on a strong authentication system. While passwords, PINs, or pattern locks can overwhelm users, active biometric schemes like retina scans require active use and cannot be used in continuous situations. A solution to this is the use of implicit continuous biometrics such as heart rate, gait, and breathing patterns. In this work, we present two context-dependent soft-biometric-based wearable authentication system strategies utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that in a sedentary state, a binary support vector machine with radial basis function (RBF) kernel can achieve an average accuracy of $0.94 \\pm 0.07$ and $F_1$ score of $0.93 \\pm 0.08$. In a non-sedentary state, $k$- Nearest Neighbors ($k$=2) can achieve an average accuracy of $0.93 \\pm 0.06$ and $F_1$ score of $0.93 \\pm 0.03$, which shows the promise of this work. Considering the availability of a single user's data, we develop unary models and obtain an average accuracy of $0.72 \\pm 0.10$ and a $F_1$ score $0.73 \\pm 0.06$ when sedentary and $0.72 \\pm 0.10$ and a $F_1$ score $0.72 \\pm 0.09$ respective scores when non-stationary.
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650 4 $a Computer science. $3 523869
650 4 $a Information science. $3 554358
650 4 $a Bioinformatics. $3 553671
653 $a Implicit Authentication
653 $a Multi-Biometric Model
653 $a Security
653 $a Wearable User Authentication
690 $a 0984
690 $a 0723
690 $a 0715
710 2 $a Fordham University. $b Computer and Information Science. $3 3171151
773 0 $t Masters Abstracts International $g 82-09.
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791 $a M.S.
792 $a 2021
793 $a English
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