Feature selection for biometric recognition based on electrocardiogram signals

摘要

Currently the demand for the development of more precise and reliable methods of person identification have received attention from the academic community and industry, with Biometrics being one of these new approaches. The term `Biometrics' is used to refer to identification techniques based on physical or behavioural characteristics. As biometric recognition becomes increasingly popular, the fear of circumvention, obfuscation and replay attacks is a rising concern. Since the traditional biometric modalities (face, iris and fingerprint) are not able to supply the needs of every possible security requirement, numerous emerging biometric modalities are presented, trying to fill the gap. Biomedical signals, like electrocardiogram (ECG) and electroencephalogram (EEG), have been proposed as emerging biometric modalities. The advantages of using the ECG for biometric recognition can be summarized as universality, permanence, uniqueness, robustness to attacks, liveness detection. According to the utilized features, the existing ECG based biometric systems can be classified to fiducial, non-fiducial and hybrids systems. This papers analyses the impact of some feature selection strategies like Genetic Algorithm, Memetic Algorithm and Particle Swarm Optimization on the performance of Biometric Systems based on ECG using K-Nearest Neighbours, Support Vector Machines, Optimum Path Forest and a Euclidean Distance Classifier for classification task. The results show that there is a subset of features extracted from the ECG signal that provides high recognition rates.