This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively. 1. Introduction Many body parts, signaling methods, and behavioral characteristics have been suggested and used for biometrics. It includes facial characteristics, digital fingerprints, retinal scans, gait, voice patterns, and handwritten signatures [1]. Biometric identifiers are distinctive to an individual and are considered more reliable and capable than the traditional possession or knowledge based technologies in differentiating between an authorized and a fraudulent person. The biometric technology is being increasingly popular, but the concerns of this technology include the reproduction of falsified credentials from an original biometric sample, removal of the biometric features for restricting the establishment of a true identity, and presentation of the original biometric sample from an illegitimate subject. The reasons that conventional biometrics are not robust enough against falsification due to their characteristics are that they are neither confidential nor secret to an individual. For example, faces are publically visible, irises pattern can be observed anywhere they look, fingerprints are left on everything they touch, voices are being recorded, and handwritten signature can be falsely replicated [2]. In order to compliance the need of a practical biometric system such as low error rate to achieve high security level and the detection of fake biometric
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