An electrocardiogram (ECG) documents the voltage changes during heartbeats. It captures electrocardiographic signals in a noninvasive way. ECGs are complicated and vary from person to person, making them ideal for use in biometric recognition systems. A number of studies have shown that ECG signals are nonlinear curves and dynamically chaotic. The ECG signals were measured on the basis of the Einthoven’s triangle principle in this study. Combining captured ECG signals using ECG biosensors and a data acquisition (DAQ) card, LabVIEW was used to design a human–machine interface (HMI) to display the processed ECG signals for test subjects. The saved ECG data were plotted in a dynamical map of the chaotic dynamic error using a master–slave chaotic system. The chaotic eye was selected as a feature and an identity database was built using an element model. Personal identity was identified by categorizing with an extension method. Thirty-six subjects were tested and the identification accuracy was 94.4%. The MIT-BIH Normal Sinus Rhythm Database (NSRDB) and an arrhythmia database were used in this study. Using the extension method, the classification accuracy between normal and cardiac arrhythmia was 91.67%, and the accuracy was increased to 100% when matter element extensibility was employed. Results suggested that the biometric recognition method developed in this study performs identification rapidly with high positive recognition rate and reliability.