Multi-view discriminant analysis with sample diversity for ECG biometric recognition

Abstract

Currently, electrocardiogram (ECG) biometric recognition is a novel research trend, and many methods have been developed. Due to the influence of physical and psychological activities, there are heartbeats diversities of the same person. However, the existing ECG biometric recognition methods do not make use of sample diversity information. In this paper, we present a multi-view discriminant analysis approach in the consideration of sample diversity for ECG biometric recognition. Firstly, we propose a method of generating multiple views by using single lead ECG signal. Secondly, we present a multi-views learning framework, which takes sample diversity into account to generate a more discriminative subspace. Thirdly, to obtain a more robust solution, we introduce a denoising constraint to learn the relationships between different views, which can create a stable representation against ECG noise. At last, experimental results demonstrate that compared with the state-of-the-art methods on four databases, the proposed method can achieve competitive performance compared to state-of-the-art ECG biometric recognition methods.