Variability in ECG Biometrics: State of the Art and Subspace Methods

This work addresses the challenges of evaluating electrocardiogram (ECG) biometric recognition systems. While ECG has attracted significant interest, most of the prior art have approached it from the perspective of a typical biometric modality and have neglected the physiological factors that affect the respective systems. This work presents the UofT ECG Database (UofTDB) and offers a comprehensive analysis of the underlying inter-individual variability under a number of conditions such as body posture, physical activity and time-lapse. The performance of various methodologies is reported under the above mentioned conditions and a method based on template fusion is proposed to address these shortcomings. Furthermore this work proposes a ECG-based posture-detection algorithm and state-detection verification method to overcome the problem of verification under uncontrolled posture settings. Finally we evaluate different subspace methodologies for ECG recognition under different ECG variability to establish a baseline performance for future works.M.A.S