Date of Award
Santa Clara : Santa Clara University, 2020.
Computer Science and Engineering
Biometric authentication has proven to be a reliable form of security as we have seen its continuous acceptance in multiple areas of technology. This includes facial recognition, fingerprints, gait analysis, and others. In this project, we develop a system that will provide authentication via electrocardiogram (ECG) signals. This system will employ a supervised machine learning approach to connect the various features generated by an ECG signal with the user. We will collect data using an online database. We will use this data to train the machine learning model that will be used to authenticate. Once a user is authenticated, then the user will be allowed to have access to any devices for which they are registered users. This system will provide a new approach for authentication that will improve the overall security of its users.
Miles, Austin and Garcia, Carlos, "Biometric Authentication via Electrocardiogram Signals" (2020). Computer Science and Engineering Senior Theses. 160.