The applications of modern biometric techniques for person identification systems rapidly increase for meeting the rising security demands. The distinctive physiological characteristics are more correctly measurable and trustworthy since previous measurements are not appropriately made for physiological properties. While a variety of strategies have been enabled for identification, the electrocardiogram (ECG)-based approaches are popular and reliable techniques in the senses of measurability, singularity, and universal awareness of heartbeat signals. This paper presents a new ECG-based feature extraction method for person identification using a huge amount of ECG recordings. First of all, 1800 heartbeats for each of the 36 subjects have been obtained from the widespread and large MIT-BIH database (MITDB) downloaded from the PhysioBank archive. Then the fiducial points of each heartbeat were determined and fourteen different features were extracted utilizing these fiducial points. Next, the polynomial curve fitting-based dimension reduction technique was employed on the extracted fourteen features. Furthermore, six celebrated classifiers including artificial neural networks (ANNs), decision trees (DTs), Fisher linear discriminant analysis (FLDA), K-nearest neighbors (K-NNs), naive Bayes (NB), and support vector machines (SVMs) were applied for the verification and performance evaluation of the proposed study. Also, as a different classifier, temporal classification and random forest was utilized for a benchmark classification. The highest performance was attained with 95.46\% accuracy rate in the case of the SVM classifier. The experimental results emphasize that the proposed ECG-based feature extraction method gives insightful merit for biometric-based person authentication systems.