EMG Biometric Verification Via Disentangled Representations

Electromyography (EMG) with individually unique characteristics, has emerged as a promising biometric trait. The capability to further encrypt EMG biometric patterns via distinct muscle activities (serve as a password), characterizes EMG biometrics with both a high recognition accuracy and revocability. The biometric component and the password component together form the global patterns of EMG. Previous EMG biometric verification methods directly extracted features from EMG signals to form global EMG representations with the biometric and password components entangled together. In this work, a disentanglement model was applied to disentangle the global EMG representations into password-specific and biometric-specific components in two separate latent spaces. The disentanglement model was built on a multibranch-encoder and single-decoder architecture. The two disentangled representations were learned separately by two cascaded support-vector domain description (SVDD) models. The model was trained and tested with data acquired on different days, to validate the interday robustness of our system, which is important for biometric verification using variable physiological signals. Results demonstrated that learning from disentangled representations contributes to a better EMG biometric verification performance compared with learning directly from the global representation. Our method achieved an Equal Error Rate (EER) of 0.0075 when impostors do not know the passwords. Furthermore, even when the impostors know the password, the biometric defense alone still managed to prevent intrusion with an EER of 0.1582. To the best of our knowledge, this is the first study to employ disentangled EMG representations for biometric verification.