Electromyography-based Biometrics for Secure and Robust Personal Identification and Authentication

Abstract

Recently, electromyogram (EMG), the electrical activity of skeletal muscles, has been proposed as a novel biometric trait to address the limitations of current biometrics, such as fingerprint and facial recognition. A unique property of EMG as a biometric trait is that it allows for distinguishable patterns from different limb movements (e.g. hand gestures), enabling individuals to set personalized passwords comprising multiple gestures for dual-security systems, i.e., both biometric-level and password-level. This is fundamentally different from other physiological signals such as electrocardiogram (ECG) and electroencephalogram (EEG), which are highly difficult for the user to voluntarily control with sufficient precision. This unique advantage has facilitated EMG-based biometrics for two different applications: authentication, where a user can access personal devices, and identification, where the system determines the closest match within a database. To establish EMG as a novel biometric trait, the following two properties need to be thoroughly investigated: 1) the ability to accurately detect the genuine user from all the other users (uniqueness), and 2) retaining the biometric performance over multiple sessions and multiple days (robustness). The overarching aim of this PhD research is to investigate these properties by addressing a series of research questions in the following studies. In the first study (Chapter 3), the effect of EMG system parameters such as the feature extraction methods and the number of channels are investigated for improved biometric performance. Three robust feature extraction methods, Time-domain (TD), Frequency Division Technique (FDT), and Autoregressive (AR) features, and their combinations were investigated, while the number of channels varied from one to eight. The results showed that for all the feature extraction methods, the performance of a four-channel setup plateaued with a further increase in channels. For a four-channel system, the authentication performance resulted in an average equal error rate (EER) of 0.04 for TD features, 0.053 for FDT features, and 0.10 for AR features. The identification mode resulted in an average Rank-1 accuracy was 97% for TD features, 87.6% for FDT features, and 63.7% for AR features. Thus, combining the TD feature set and a four-channel EMG is recommended for optimal biometric performance. In the second study (Chapter 4), the dual-security property of EMG is facilitated by the development of a multi-code framework. Such a framework allows the combination of hand gestures to form an access code. In this study, three levels of fusion, score, rank, and decision were investigated for the two biometric applications. The biometric performance of the fusion schemes wasanalyzed while varying the codelength from one to six. For a codelength of four, the authentication EER was 0.006 using a decision-level fusion scheme using a weighted majority voting. For the identification mode, the score-level fusion scheme resulted in a Rank-2 accuracy of 99.9% for a codelength of four. The multi-code biometric system provided improved dualmode security based on the personalized codes and biometric traits of individuals. However, the above two studies and the majority of the current EMG-based biometric research face two critical limitations: 1) a small subject pool, comparative to other more established biometric traits, and 2) single-session data sets. In multi-day scenarios, there is performance degradation of EMG-based biometrics. In the third study (Chapter 5) a multi-day and large-sample dataset collection was performed to address these limitations. For the research study, EMG data was collected from 43 participants over three different days with long separation (Days 1, 8, and 29) while performing 16 different static hand/wrist gestures with seven repetitions. The dataset was made public as the GRABMyo dataset. In study four (Chapter 6), a multi-day analysis involving training data and testing data from different days of the GRABMyo dataset was employed to test the robustness of the EMG-based biometrics in practical scenarios. The cross-day authentication using the FDT features extraction resulted in a median EER of 0.039 when the code (gestures) was secure, and an EER of 0.068 when the code (gestures) was leaked to intruders. The cross-day identification achieved a median rank-5 accuracy of 93.0%. For improving multi-day performance, robust feature extraction methods that employ deep learning are warranted. In study five (Chapter 7), a convolutional feature engineering method, MyoBM-Net, is proposed. It involves a two-stage training paradigm for improving the authentication performance. In a cross-day analysis, the MyoBM-Net resulted in a median EER of 0.003 and 0.008 when the gesture (code) is safe and compromised, respectively, thus suggesting superior performance than the traditional feature extraction method. The findings suggest that the performance of EMG-based biometrics is comparable to conventional biometrics for both authentication and identification applications. The results show the potential of using EMG signals for biometric identification in real-world scenarios. The multi-code framework facilitates the combination of gestures as passcodes. The large multi-day dataset will support further research on EMG-based biometrics and other gesture recognition applications. The MyoBM-Net architecture will enable the development of new applications using the GRABMyo dataset, leading to accurate and robust biometric performance. This could lead to EMG-based biometrics being used as an alternative to traditional biometric methods.