CardioID: Learning to Identification from Electrocardiogram Data

Abstract Human identification is an important task that can help protect information security. Building deep learning models for human identification from Electrocardiogram (ECG) data is one of the highly promising technique. It has several unique advantages such as liveness detection, insensitive, easy to collect, higher security and so on. However, existing classifier-based methods only support closed-set identification, while existing matching-based methods are limited to high computational complexity. Besides, almost all methods only consider one-shot identification, which might be affected by occasional noise. In this paper, we propose CardioID to solve the above problems. CardioID learns binary codes from continuous ECG data which can identify faster than existing methods. It also supports identifying new person without the need to reconstructed or re-train the model. Besides, it can theoretically guarantee the recognition accuracy by introducing statistical hypothesis testing for making an identification decision. Experiments on real world ECG data show that CardioID can achieve 9.84% higher identification accuracy while saving 30.90% of running time compared with each of the second best baselines.