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Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics Using OpenBCI

This paper on surface electromyography (sEMG)-based biometrics obtained via the OpenBCI Cyton board and GUI was published January 11th by Qingqing Li, Penghui Dong, and Jun Zheng at the Department of Computer Science and Engineering, New Mexico Institute of Mining and Technology to Appl. Sci. 2020. The research tested and validated the use of biosensing as part of two-factor authentication for mobile devices.

Abstract:

“Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock.”

If you’ve published research using OpenBCI (or know someone who has) and it’s not in the Citation List, please let us know! Send us a note at [email protected]

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