User authentication is an important security mechanism to prevent unauthorized accesses to systems or devices. In this paper, we propose a new user authentication method based on surface electromyogram (sEMG) images of hand gestures and deep anomaly detection. Multi-channel sEMG signals acquired during the user performing a hand gesture are converted into sEMG images which are used as the input of a deep anomaly detection model to classify the user as client or imposter. The performance of different sEMG image generation methods in three authentication test scenarios are investigated by using a public hand gesture sEMG dataset. Our experimental results demonstrate the viability of the proposed method for user authentication.
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Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
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.
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- Award ID(s):
- 1757207
- PAR ID:
- 10130858
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 10
- Issue:
- 2
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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User authentication plays an important role in securing systems and devices by preventing unauthorized accesses. Although surface Electromyogram (sEMG) has been widely applied for human machine interface (HMI) applications, it has only seen a very limited use for user authentication. In this paper, we investigate the use of multi-channel sEMG signals of hand gestures for user authentication. We propose a new deep anomaly detection-based user authentication method which employs sEMG images generated from multi-channel sEMG signals. The deep anomaly detection model classifies the user performing the hand gesture as client or imposter by using sEMG images as the input. Different sEMG image generation methods are studied in this paper. The performance of the proposed method is evaluated with a high-density hand gesture sEMG (HD-sEMG) dataset and a sparse-density hand gesture sEMG (SD-sEMG) dataset under three authentication test scenarios. Among the sEMG image generation methods, root mean square (RMS) map achieves significantly better performance than others. The proposed method with RMS map also greatly outperforms the reference method, especially when using SD-sEMG signals. The results demonstrate the validity of the proposed method with RMS map for user authentication.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app10020541
