Siamese network‐based user‐independent model for surface electromyogram biometric authentication
Youngsam Kim, Jong‐hyuk Roh, Soo-Hyung Kim · ETRI Journal · 2025
Abstract The advent of deep‐learning technology has enhanced the performance of biometric systems, including facial and fingerprint recognition systems. Although facial recognition is now commonly used for authenticating mobile device users, it can be easily falsified as it relies on external traits. In this study, we design a user‐independent model for surface electromyogram (sEMG) biometric authentication using the convolutional Siamese network with an N ‐pair loss function. We then implement the shift‐equivariant model by exploiting the convolution and padding operations to deal with small shifts in multichannel sEMG sensors for various users. Additionally, the augmentation methods for time‐series data and spectrograms are used to further improve the performances of the model. We employ the public Gesture Recognition and Biometrics electroMyogram (GRABMyo) dataset, comprising 43 subjects and 16 gestures collected over 3 days, to train and evaluate the model. The proposed model achieves equal error rates of 5.62% and 8.94% for unknown subjects while preserving and leaking the gesture code, respectively. In cross‐day experiments, the model achieves rates of 4.92% and 7.56%, respectively, demonstrating robustness to intersession variations.