LAUSR

ECG data are biosignals with unique characteristics that can be obtained regardless of time and space constraints. Owing to these advantages, they have been widely used for not only diagnosing diseases but also recognizing people. Numerous studies have been conducted and various feature vectors from a large amount of data have been suggested to improve recognition performance. The key to extracting feature vectors is to extract differences in one-dimensional ECG signals without loss in order to recognize human identity. In this paper, we propose new feature vectors based on fiducial points. These feature vectors have simple and clear shapes that combine temporal and amplitude information. The discriminator operating in the proposed human identification system measures distance-based similarity. This method alleviates computational burden and enables the human identification system to run in real time. Based on the system, we conducted a number of recognition experiments. The experimental results proved that the proposed feature vectors are valid information that represents significant differences between individuals. In the experiments with 100 subjects, we obtained a recognition rate of over 94% when two or more than two heartbeat signals were used, and confirmed that as the number of input heartbeats increased the performance also improved proportionally.