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Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication

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...

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Autores principales: Kumar, Pradeep, Saini, Rajkumar, Kaur, Barjinder, Roy, Partha Pratim, Scheme, Erik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864782/
https://www.ncbi.nlm.nih.gov/pubmed/31661761
http://dx.doi.org/10.3390/s19214641
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author Kumar, Pradeep
Saini, Rajkumar
Kaur, Barjinder
Roy, Partha Pratim
Scheme, Erik
author_facet Kumar, Pradeep
Saini, Rajkumar
Kaur, Barjinder
Roy, Partha Pratim
Scheme, Erik
author_sort Kumar, Pradeep
collection PubMed
description 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.
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spelling pubmed-68647822019-12-06 Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication Kumar, Pradeep Saini, Rajkumar Kaur, Barjinder Roy, Partha Pratim Scheme, Erik Sensors (Basel) Article 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. MDPI 2019-10-28 /pmc/articles/PMC6864782/ /pubmed/31661761 http://dx.doi.org/10.3390/s19214641 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kumar, Pradeep
Saini, Rajkumar
Kaur, Barjinder
Roy, Partha Pratim
Scheme, Erik
Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication
title Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication
title_full Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication
title_fullStr Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication
title_full_unstemmed Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication
title_short Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication
title_sort fusion of neuro-signals and dynamic signatures for person authentication
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864782/
https://www.ncbi.nlm.nih.gov/pubmed/31661761
http://dx.doi.org/10.3390/s19214641
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