Exploring EEG-based biometrics for user identification and authentication

Gui, Qiong; Jin, Zhanpeng; Xu, Wenyao

doi:10.1109/spmb.2014.7002950

Cited by 107 publications

(57 citation statements)

References 31 publications

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“…However, it is a time consuming and expensive process. Moreover, brain signals for specific task might change during different circumstances and even it is possible for a person to change his/her own brainwave pattern (Khalifa et al 2012), (Gui et al 2014). …”

Section: Brainwave or Eegmentioning

confidence: 99%

A Survey on Biometrics and Cancelable Biometrics Systems

Choudhury

Then

Issac

et al. 2018

Int. J. Image Grap.

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Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results.Key Words: Biometrics, Cancelable Biometrics, Security, Biometric Salting, Non-Invertible Transformation INTRODUCTIONThe biometric traits possessed by each individual are unique and has the potential to recognize an individual. Biometric traits can be physical and behavioral. Therefore, biometrics are used for authentication or recognition of individuals for many critical applications like border control, access control, immigration, forensic and different law enforcement. There are two phases in every conventional biometric system: enrolment phase and authentication phase. In the enrolment stage, the original biometric trait is captured and saved in the database. During the authentication stage, the system matches that stored template every time the user access the system by providing the live biometric (Kaur et al. 2014).Compared to password or token based authentication system, biometric system using fingerprint, iris, face, voice, etc. provides better security as people cannot lose or forget their biometric trait. But, the advanced technology of today's world makes it possible to create a loophole in the biometric system. People leave their fingerprints on whatever they touch; hence one can easily steal the fingerprint and can even make an artificial finger using the stolen fingerprint. The person's face can be captured by the camera even from a distance without their concern. In such situation, the security of the biometric based authentication system is at a stake.To overcome the problem of the stolen biometrics, the researchers have developed the template protection schemes. The biometric template protection schemes are mainly divided into two categories: i) Biomet...

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Section: Brainwave or Eegmentioning

confidence: 99%

A Survey on Biometrics and Cancelable Biometrics Systems

Choudhury

Then

Issac

et al. 2018

Int. J. Image Grap.

View full text Add to dashboard Cite

show abstract

“…There are many techniques that can be used to decrease the noise for EEG, such as ensemble averaging and correlation. The ensemble averaging uses multiple measurements and is effective and efficient in reducing the noise from the collected signals (Gui et al, 2014). Finally, there is correlation, which is a mathematical operator where the signals serve as the inputs of the operator that produces a third signal named cross-correlation coefficient of the two input signals.…”

Section: Pre-processingmentioning

confidence: 99%

“…Encouragingly, the new Electroencephalogram (EEG) based biometrics, which represents human brain activities, has emerged as an approach to human identification (Gui et al, 2014). EEG records an individual's brain activity through some measurements.…”

Section: Introductionmentioning

confidence: 99%

A Review Study of Brian Activity-Based Biometric Authentication

Alariki¹,

Ibrahimi²,

Wardak³

et al. 2018

Journal of Computer Science

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Biometrics is the process of identifying an individual among others by biological means. Concerning security, biometric system is one of the best options available in this technology driven era. Places such as nuclear facilities, airports, banks etc. are on the frontline of security threats. Therefore, biometrics such as Iris, face and fingerprint recognition is frequently used to avoid any security breach. However, the possibility of imitating, replicating or even the stealing of original data has made these tools unreliable. As a result, there has been a growing interest in finding a better biometric system and brain activitybased biometrics such as Electroencephalography (EEG) and Functional Near-Infrared Spectroscopy (fNIRS) come with the advantage of being quite impossible to mimic. This paper presents a thorough and in depth review of the state of the art studies and research on brain activity-based biometrics. These studies and selected research projects are reviewed based on their feature extraction, methods, classification and most importantly, performance. Reviewing the most recent studies and research, we have found that time and frequency based features are better to be considered together for a brain activity-based biometric system. Together they are effective and efficient and give us a higher performance rate. Furthermore, we have found that Support Vector Machine (SVM) classifier is the best classification option with 100% accuracy and it can be used for a higher number of users for a biometric system. Our review lays a foundation for future investigation into the use of a combination of EEG and fNIRS for a biometric based authentication system.

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“…They are used for detecting ECG signals (Gui et al, 2014), KARTBAEV T ET AL: Identity authentication signature (Hatkar et al, 2015), and identification of the palm of a smartphone user (Hassanat et al, 2015). In the identity authentication systems, the mathematical apparatus of artificial NN are also widely used.…”

Section: Introductionmentioning

confidence: 99%

Development of a Computer System for Identity Authentication Using Artificial Neural Networks

et al. 2017

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The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics' changes over time (e.g., due to aging). The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

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Exploring EEG-based biometrics for user identification and authentication

Cited by 107 publications

References 31 publications

A Survey on Biometrics and Cancelable Biometrics Systems

A Survey on Biometrics and Cancelable Biometrics Systems

A Review Study of Brian Activity-Based Biometric Authentication

Development of a Computer System for Identity Authentication Using Artificial Neural Networks

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