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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5594
Title: Fusion of neuro-signals and dynamic signatures for person authentication
Authors: Kumar P.
Saini R.
Kaur B.
Pratim Roy, Partha
Scheme E.
Published in: Sensors (Switzerland)
Abstract: Many biometric systems based on physiological traits such as ones facial characteristics, iris, and fingerprint have been developed for authentication purposes. Such security systems, however, commonly suffer from impersonation attacks such as obfuscation, abrasion, latent samples, and covert attack. More conventional behavioral methods, such as passwords and signatures, suffer from similar issues and can easily be spoofed. With growing levels of private data readily available across the internet, a more robust authentication system is needed for use in emerging technologies and mobile applications. In this paper, we present a novel multimodal biometric user authentication framework by combining the behavioral dynamic signature with the the physiological electroencephalograph (EEG) to restrict unauthorized access. EEG signals of 33 genuine users were collected while signing on their mobile phones. The recorded sequences were modeled using a bidirectional long short-term memory neural network (BLSTM-NN) based sequential classifier to accomplish person identification and verification. An accuracy of 98.78% was obtained for identification using decision fusion of dynamic signatures and EEG signals. The robustness of the framework was also tested against 1650 impersonation attempts made by 25 forged users by imitating the dynamic signatures of genuine users. Verification performance was measured using detection error tradeoff (DET) curves and half total error rate (HTER) security matrices using true positive rate (TPR) and false acceptance rate (FAR), resulting in 3.75% FAR and 1.87% HTER with 100% TPR for forgery attempts. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Citation: Sensors (Switzerland) (2019), 19(21): -
URI: https://doi.org/10.3390/s19214641
http://repository.iitr.ac.in/handle/123456789/5594
Issue Date: 2019
Publisher: MDPI AG
Keywords: Authentication
Biometrics
Dynamic signature
Electroencephalography (EEG)
Identification
Smartphone
ISSN: 14248220
Author Scopus IDs: 57212043589
57190288840
57208659024
56880478500
57202922022
Author Affiliations: Kumar, P., Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Saini, R., Department of Computer Science & Engineering, Indian Institute of Technology, Roorkee, 247667, India
Kaur, B., Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Roy, P.P., Department of Computer Science & Engineering, Indian Institute of Technology, Roorkee, 247667, India
Scheme, E., Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Funding Details: Funding: The research was supported by the New Brunswick Innovation Foundation.
Corresponding Author: Kumar, P.; Institute of Biomedical Engineering, University of New BrunswickCanada; email: pkumar1@unb.ca
Appears in Collections:Journal Publications [CS]

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