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Data from: Enhancing the security of pattern unlock with surface EMG-based biometrics

Citation

Li, Qingqing; Dong, Penghui; Zheng, Jun (2020), Data from: Enhancing the security of pattern unlock with surface EMG-based biometrics, Dryad, Dataset, https://doi.org/10.5061/dryad.dfn2z34xs

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 F 1 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.

Methods

Subjects: The data collecting process was approved by the Institutional Review Board (IRB) of the New Mexico Institute of Mining and Technology. We recruited 10 subjects from the school (7 males and 3 females, age = 23.8 ± 2.5 years, height = 173.9 ± 9.7 cm, weight = 70.0 ± 15.6 kg, all right-handed) to participate at the data collecting process. All participants reported that they did not have upper limb musculoskeletal and nervous system diseases.

Acquisition: The OpenBCI cyton board was used to collect the sEMG generated by using the right-hand index finger to draw the unlock pattern on an Android mobile phone. The Cyton biosensing board (OpenBCI) recorded finger movement through a gold cup electrode (OpenBCI) placed on the FDS muscle of each subject’s forearm. The sampling rate of the Cyton board is 250 Hz. The acquired sEMG signal is then sent from the Cyton board to the Cyton Dongle (OpenBCI), a Bluetooth adaptor plugged into a laptop. The OpenBCI GUI software installed on the laptop then records the acquired data in the local storage.

Each subject was instructed to draw two patterns. Each pattern was drawn repeatedly for 20 trials. sEMG signals were recorded for each trial. The subject was instructed to rest the arm on the table for one minute before the next trial to avoid muscle fatigue. The whole process for a subject lasted about one hour and twenty minutes, which consisted of 40 trials for the two patterns.

Pre-processing: The quality of the recorded raw sEMG signals was affected by noises, such as direct current offset, environmental noises, and artifact noises. We applied a 5Hz high-pass filter to eliminate the direct current offset, baseline drift due to the movement in recording, and perspiration. A 60Hz notch filter was then applied to filter out the power-line noise.

Usage Notes

The dataset contains 400 data files corresponding to the 400 trials of the 10 subjects. Each data file is a 6s sEMG signal segment extracted after the pre-processing starting from the time when the subject started the drawing of the unlock pattern. Therefore, each data file consists of 1500 data points. In our study, time-domain features were extracted from the signal segment for authentication purpose.

The data files are organized in 10 directories corresponding to the 10 subjects. In each directory, the file is named as pp_tt.csv, where pp is the pattern number, and tt is the trial ID. For example, in directory 01, p1_01.csv contains the sEMG signal segment from subject 1 drawing pattern 1 in the first trial.

For publication purposes, the data for each subject have been compressed into zip files. Zipped filenames follow the patter:

li_dong_zheng_2020_pattern_unlock_subject_NN_sEMG.zip

where NN is given as the subject's number (01-10).

Funding

EPSCoR Cooperative Agreement, National Science Foundation, Award: OIA-1757207

Center for Hierarchical Manufacturing, National Science Foundation, Award: OIA-1757207

EPSCoR Cooperative Agreement, National Science Foundation, Award: OIA-1757207