Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018184" target="_blank" >RIV/62690094:18450/21:50018184 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0957417421000245?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417421000245?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2021.114583" target="_blank" >10.1016/j.eswa.2021.114583</a>

Result language

angličtina

Original language name

Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection

Original language description

This paper presents a human verification scheme in two independent stages to overcome the vulnerabilities of attacks and to enhance security. At the first stage, a hand image-based CAPTCHA (HandCAPTCHA) is tested to avert automated bot-attacks on the subsequent biometric stage. In the next stage, finger biometric verification of a legitimate user is performed with presentation attack detection (PAD) using the real hand images of the person who has passed a random HandCAPTCHA challenge. The electronic screen-based PAD is tested using image quality metrics. After this spoofing detection, geometric features are extracted from the four fingers (excluding the thumb) of real users. A modified forward–backward (M-FoBa) algorithm is devised to select relevant features for biometric authentication. The experiments are performed on the Boğaziçi University (BU) and the IIT-Delhi (IITD) hand databases using the k-nearest neighbor and random forest classifiers. The average accuracy of the correct HandCAPTCHA solution is 98.5%, and the false accept rate of a bot is 1.23%. The PAD is tested on 255 subjects of BU, and the best average error is 0%. The finger biometric identification accuracy of 98% and an equal error rate (EER) of 6.5% have been achieved for 500 subjects of the BU. For 200 subjects of the IITD, 99.5% identification accuracy, and 5.18% EER are obtained. © 2021

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

Expert systems with applications

ISSN

0957-4174

e-ISSN

—

Volume of the periodical

171

Issue of the periodical within the volume

Jun

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

"Article number 114583"

UT code for WoS article

000634864500002

EID of the result in the Scopus database

2-s2.0-85100141153