Electrocardiograms are generated by electrical signals, and therefore individual electrocardiogram signals can vary according to the measurement environment that is created by the subjects' behavioral characteristics. Generally, post-exercise electrocardiograms that are used as biometric identification data do not match pre-exercise electrocardiograms owing to temporarily occurring tachycardia, and this reduces user identification performance. Research is being conducted on normalization techniques that make the post-exercise electrocardiograms match the pre-exercise electrocardiograms, but there have been problems caused by the distortion of morphological features such as P waves, QRS complexes, and T waves. To address problem, this study looked at the measurements of electrocardiograms before and after exercise and performed linear interpolation normalization on the P and T waves of a post-exercise tachycardia electrocardiogram cycle to make it match the pre-exercise electrocardiogram cycle. This study also proposes a user recognition system based on this technique. To analyze the proposed system's performance, the lead-I electrocardiogram signals of 20 subjects were measured three times at 2- to 3-day intervals before and after exercise, and a database was constructed. The experiment results showed that the conventional algorithm's maximum recognition performance was 88.33%, and the proposed normalization method's performance was 91.67%. A combined method was found to be both excellent and similar to the proposed method, with a performance of 92.5%.
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