With the rise of the IoT and the usage of mobile devices, the need for improved security for those devices becomes more critical. Beyond regular passwords several other forms of identification such as biometric identification, have been introduced. They can offer increased convenience and less vulnerability to spoofing attacks. Most common forms of applied biometric identification include iris, face and fingerprint scanners that see most use in smartphones. But there has been an increasing interested in methods that utilize physiological signals of the human body. electrocardiogram (ECG) and photoplethysmogram (PPG) are among them and are the main point of interest for this work. They come with inherent advantages like being difficult to reproduce and can not be forgotten like a password.

Gathering records of the two signal types has become easier over the years and can now be performed with wearables like the Apple Watch. This opens new options for this field of research.

My work focuses on analyzing and reimplementing existing approaches for ECG and PPG based biometric identification systems and comparing them to deduct similarities, differences, strengths and weaknesses.

To achieve this two convolutional neural network (CNN) based ECG implementations and one PPG implementation that utilizes handcrafted feature extraction were adapted to work on a shared dataset that contain synchronized ECG & PPG data from the private SAPE and the public BIDMC database. This database was then used for evaluation of the systems. In addition commonly used biometric methods and databases were analyzed to aid in the final evaluation. High rates of accuracy were reached and compared to literature that utilized similar datasets.