PBGAN: Learning PPG Representations From GAN for Time-Stable and Unique Verification System

Hwang, Dae Yon; Taha, Bilal; Hatzinakos, Dimitrios

doi:10.1109/tifs.2021.3122817

Cited by 12 publications

(2 citation statements)

References 37 publications

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“…Biswas et al [4] proposed a deep-learning framework for PPG-based heart-rate estimation and biometric identification in ambulant environments that used a four-layer deep neural network, including two CNN layers and two LSTM layers. Hwang et al [23] used a generative adversarial network to create synthetic person-specific and time-stable PPG signals for PPG biometric recognition for verification scenarios. Ye et al [24] trained a CNN and LSTM network as the feature extractor and identified PPG signals by Mahalanobis distance and cosine similarity.…”

Section: Ppg Biometric Recognitionmentioning

confidence: 99%

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Time–frequency fusion learning for photoplethysmography biometric recognition

Liu

Huang

et al. 2022

IET Biometrics

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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.

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Section: Ppg Biometric Recognitionmentioning

confidence: 99%

“…Hwang et al. [23] used a generative adversarial network to create synthetic person‐specific and time‐stable PPG signals for PPG biometric recognition for verification scenarios. Ye et al.…”

Section: Related Workmentioning

confidence: 99%