• B.Harshitha
• G. Venkata Sunil Reddy
• G.Priyanka
• B. Yaswanth
• S. Saravanan

Now-a-days the major difficult task is to protect the integrity of the devices and the sensitive data. In this proposed method Electrocardiogram (ECG) is used which measures the electrical activity of heart. It provides a powerful authentication system for person recognition due to the variation in ECG signatures. Compared with the fingerprint biometrics and face authentication the biometric system based in ECG provides much pace and high security.ECG (Electrocardiogram) biomatric authentication is a methode of using an individual’s uniqe ECG signals to verify their identity .This type of biometric authentication is becoming increasingly popular due to the high level of security it provids strong authentication and recognition methods become an indispensable immediate job to protect the integrity of the devices and the sensitive data. Passwords have provided access management and hallmark, but have shown their natural vulnerabilities. The speed and comfort factor are what makes biometrics the excellent authentication answer as they could have a lower probability of circumvention.The rise of electrocardiograms (ECGs) has led to the development of new biometric systems that are more secure and easy to use. Due to their unique nature, they are considered to be the most popular choice for replacing traditional passwords. However, one of the biggest challenges that the development of these systems faces is the lack of a large database. In this report, we contribute to creating a new big gallery off- the- person ECG data that can provide new opportunities for the ECG biological study community. We explore the impact of filtering category, classification, feature removal, and health standing on ECG fingerprint by using the evaluation metrics.Our outcomes have shown that our ECG biometric authentication outperforms existing techniques lacking the ability to effectively remove features, filtering, classification, and matching.

ECG datasets, Kalman filtration, feature extraction, segment, off-the person, and on-people biometrics.

"ECG BIOMETRIC AUTHENTICATION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.c778-c786, April-2023, Available :http://www.jetir.org/papers/JETIR2304295.pdf

"ECG BIOMETRIC AUTHENTICATION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppc778-c786, April-2023, Available at : http://www.jetir.org/papers/JETIR2304295.pdf