Advancing ECG Biometrics Through Vision Transformers: A Confidence-Driven Approach

Over the past two decades, Electrocardiography (ECG) has gained significant momentum in the field of biometrics, offering a compelling alternative for person identity recognition based on physical/biological traits. Its inherent difficulty to be circumvent and its ability to enable liveness detection make it particularly appealing compared to other popular identifiers such as face, fingerprint, and iris. As a result, ECG has garnered attention from the computer vision community working on biometrics applications. We present a novel biometric method for personal recognition that leverages I-lead signals acquired off-the-person. Through fine-tuning a pre-trained Vision Transformer (ViT) model, we achieve remarkable results in recognizing individuals based on a single 2D image of their ECG recording obtained from as little as three heartbeats. Extensive evaluation on the CYBHi database, with enrollment and testing phases separated by a three-month time window, simulating a real-world, long-term identification scenario, demonstrates the robustness of our multiclass approach. Specifically, our system achieves a remarkable single sample-based identification accuracy of over 70% with a pool of 63 individuals, along with an equal error rate of only 0.48% in the 1-vs-1 authentication task. Additionally, we evaluated our approach on the very recent Heartprint database to assess the robustness of our approach with more subjects, larger separation time windows, and continuous training settings, obtaining again remarkable performance with respect to the state-of-the-art. While the promising capabilities of ECG-based biometrics are evident, given various security challenges, using such methods as standalone authentication could raise caution among users. To address this concern and enhance the system's dependability, we introduce a confidence-based rejection rule. Integrating this mechanism improves both identification and authentication performances, while it could also enable the system to detect out-of-database individuals.