A Novel Deep Metric Learning-Based State-Stable and Noise-Aware Biometric Authentication Framework Using Seismocardiogram Signals

Abstract

Biometric authentication based on different physiological signals has attracted significant attention in the last decade due to advancements in wearable sensors and communication technologies apart from the traditional ways of recognition based on fingerprint, face. Recently, researchers have been allured by photoplethysmograph (PPG)-based biometric authentication owing to its non-invasiveness, low cost, and no use of adhesive, unlike widely used electrocardiogram (ECG)-based authentication. However, the identification accuracy (IA) severely deteriorates due to frequent motion artifacts. Further, it poses security issues due to few fiducial points and the compromise of live video of the subject in video-based PPG. Recently, few researchers have explored the use of seismocardiogram (SCG), another mechanical cardiac signal modality, for biometric authentication. However, these methods are unable to extract state-stable embeddings which impact the IA. To overcome, these issues, we propose a deep metric learning-based biometric authentication framework using SCGs. The proposed framework consists of the following stages: pre-processing, mel-spectrogram extraction, subject-specific-state-stable feature extraction using parameter-shared triplet neural network, embedding dictionary construction, and authentication using an intelligent cosine similarity-based authentication module. The proposed framework is evaluated using the only publicly available CEBS dataset under basal, music, and post-music states, and outperforms the existing works by achieving an IA and equal error rate (EER) of 99.79%, and 0.42%.