Time–frequency fusion learning for photoplethysmography biometric recognition

Abstract

Photoplethysmography (PPG) signal is a novel biometric trait related to the identity of people; many time-and frequency-domain methods for PPG biometric recognition have been proposed. However, the existing domain methods for PPG biometric recognition only consider a single domain or the feature-level fusion of time and frequency domains, without considering the exploration of the fusion correlations of the time and frequency domains. The authors propose a time-frequency fusion for a PPG biometric recognition method with collective matrix factorisation (TFCMF) that leverages collective matrix factorisation to learn a shared latent semantic space by exploring the fusion correlations of the time and frequency domains. In addition, the authors utilise the ℓ 2,1 norm to constrain the reconstruction error and shared matrix, which can alleviate the influence of noise and intra-class variation, and ensure the robustness of learnt semantic space. Experiments demonstrate that TFCMF has better recognition performance than current state-of-the-art methods for PPG biometric recognition.