Multimodal Biometric Authentication for VR/AR using EEG and Eye Tracking

Krishna, Vrishab; Ding, Yi; Xu, Aigong; Höllerer, Tobias

doi:10.1145/3351529.3360655

Cited by 16 publications

(11 citation statements)

References 12 publications

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“…Moreover, Krishna et al [ 58 ] explore the use of electroencephalogram (EEG) signals in user authentication. User authentication is similar to facial recognition in mobile phones.…”

Section: Tracking Technology Of Armentioning

confidence: 99%

In-Depth Review of Augmented Reality: Tracking Technologies, Development Tools, AR Displays, Collaborative AR, and Security Concerns

Syed

Siddiqui

Abdullah

et al. 2022

Sensors

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Augmented reality (AR) has gained enormous popularity and acceptance in the past few years. AR is indeed a combination of different immersive experiences and solutions that serve as integrated components to assemble and accelerate the augmented reality phenomena as a workable and marvelous adaptive solution for many realms. These solutions of AR include tracking as a means for keeping track of the point of reference to make virtual objects visible in a real scene. Similarly, display technologies combine the virtual and real world with the user’s eye. Authoring tools provide platforms to develop AR applications by providing access to low-level libraries. The libraries can thereafter interact with the hardware of tracking sensors, cameras, and other technologies. In addition to this, advances in distributed computing and collaborative augmented reality also need stable solutions. The various participants can collaborate in an AR setting. The authors of this research have explored many solutions in this regard and present a comprehensive review to aid in doing research and improving different business transformations. However, during the course of this study, we identified that there is a lack of security solutions in various areas of collaborative AR (CAR), specifically in the area of distributed trust management in CAR. This research study also proposed a trusted CAR architecture with a use-case of tourism that can be used as a model for researchers with an interest in making secure AR-based remote communication sessions.

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“…Moreover, Krishna et al [ 58 ] explore the use of electroencephalogram (EEG) signals in user authentication. User authentication is similar to facial recognition in mobile phones.…”

Section: Tracking Technology Of Armentioning

confidence: 99%

In-Depth Review of Augmented Reality: Tracking Technologies, Development Tools, AR Displays, Collaborative AR, and Security Concerns

Syed

Siddiqui

Abdullah

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…References EEG Recording Real [23], [32], [24], [43], [33], [25], [26], [34], [35], [37], [38], [40], [41], [44], [28], [45], [29], [42], [30] Motor imagery [50], [51], [46], [52], [47], [48] Real and motor imagery [60], [56], [57], [58] Rest state [61] a large dataset. However, it requires a long training time [21].…”

Section: Activities Duringmentioning

confidence: 99%

“…Algorithms SVM [50], [24], [56], [26], [46], [58], [44], [47], [28], [45], [29], [30] Random forest [23], [58], [29] Bayesian network [56] Naive Bayes [61], [25], [58] KNN [57], [43], [58], [ [50], [32], [60], [43], [33], [44], [45] CNN [51], [34], [35], [52], [37], [38], [48] LSTM [51], [38], [40], [41] RNN [38] Using these functions, a supervised ECOC was eventually trained using an SVM classifier to classify individuals with EEG testing signals. The real positive rating of 94.44% for the suggested procedure indicated a tentative trial of nine EEG records from nine participants.…”

Section: Machine Learning Referencesmentioning

confidence: 99%

See 1 more Smart Citation

EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey

et al. 2022

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An electroencephalogram (EEG) is a measurement that reflects the overall electrical activity in the brain. EEG signals are effective for biometric authentication and robust against malware attacks and any kind of fraud activities due to the uniqueness of the signals. Significant progress in research on EEG-based authentication has been achieved in the last few years, with machine learning being extensively used for classifying EEG signals. However, to the best of our knowledge, there has been no investigation into the overall progress made in such research. In this paper, the literature on the various factors involved in state-of-the-art biometric authentication systems is reviewed. We provide a thorough comparison of different machine learning biometric authentication techniques. The comparison criteria include the research objectives, machine learning algorithms, computational complexity, source of brainwaves, feature extraction methods, number of channels, and so on. Alongside the discussion of existing works, directions for future research are suggested to improve authentication accuracy. This paper provides an in-depth discussion of different advanced biometric authentication techniques, and a vivid picture of state-of-the-art machine learning-based biometric authentication techniques using EEG.

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“…They achieved a low false rejection rate (FRR) and false acceptance rate (FAR) of 0% and 1% respectively. Krishna et al, [23] incorporated EEG signal acquisition along with eye movement tracking in an augmented reality (AR) and virtual reality (VR) headset for user authentication. Bashar et al, [24] explored the combination of brain signals (EEG) and heart signals (ECG) for authentication using wavelet domain statistical features with multiple classifiers and achieved a 90.5% F1 score.…”

Section: Introductionmentioning

confidence: 99%

Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms

et al. 2021

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Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve very high accuracy but have low anti-spoofing capability. To address these issues, a novel multimodal biometric system combining EEG and keystroke dynamics is proposed in this paper. A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. A machine learning classification pipeline is developed using multi-domain feature extraction (time, frequency, time-frequency), feature selection (Gini impurity), classifier design, and score level fusion. Different classifiers were trained, validated, and tested for two different classification experimentspersonalized and generalized. For identification and authentication, 99.9% and 99.6% accuracies are achieved, respectively for Random Forest classifier using 5 fold cross-validation. These results outperform the individual modalities with a significant margin (~5%). We also developed a binary template matching-based algorithm, which gives 6X faster system with 93.64% accuracy. The proposed method can be considered secure and reliable for any kind of biometric identification and authentication.

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Multimodal Biometric Authentication for VR/AR using EEG and Eye Tracking

Cited by 16 publications

References 12 publications

In-Depth Review of Augmented Reality: Tracking Technologies, Development Tools, AR Displays, Collaborative AR, and Security Concerns

In-Depth Review of Augmented Reality: Tracking Technologies, Development Tools, AR Displays, Collaborative AR, and Security Concerns

EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey

Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms

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