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A Robust Biometric Authentication System for Handheld Electronic Devices by Intelligently Combining 3D Finger Motions and Cerebral Responses | IEEE Journals & Magazine | IEEE Xplore

A Robust Biometric Authentication System for Handheld Electronic Devices by Intelligently Combining 3D Finger Motions and Cerebral Responses


Abstract:

Rapid advancement in sensor technology through miniaturization of electronic components has enabled the consumer electronic (CE) research community including the manufact...Show More

Abstract:

Rapid advancement in sensor technology through miniaturization of electronic components has enabled the consumer electronic (CE) research community including the manufacturers to embed various utility sensors into handheld devices. In addition to traditional sensors such as Inertial Measurement Unit (IMU), camera, fingerprint or proximity, futuristic sensors such as Electroencephalogram (EEG) or Electromyography (EMG) are also being included in the next-generation CE devices. Air or touch signature-based authentication systems are common in modern CE devices. However, cerebral activities clubbed with gestures will certainly enhance the security of such authentication systems. This can help consumers from being the victims of shoulder surfing attacks. In this article, a new method is proposed to verify air signatures by analyzing finger movements and cerebral activities together with the help of sensors in next-generation CE devices. Signatures are first spotted by analyzing 3D geometrical features of the finger movement during the signing. Concurrent EEG responses are then analyzed for the verification. Hidden Markov Model (HMM) and Random Forest (RF) classifiers have been used to train the system. Experiments reveal that EEG signals are highly correlated with the finger movements during air signatures even in the presence of motion artifacts. Therefore, false-positive rates have significantly reduced as compared to the existing tracking-based methods. Verification accuracy as high as 95.5% (HMM) and 98.5% (RF) have been recorded when tested on our dataset.
Published in: IEEE Transactions on Consumer Electronics ( Volume: 67, Issue: 1, February 2021)
Page(s): 58 - 67
Date of Publication: 28 January 2021

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