Abo-Zahhad et al., 2014 - Google Patents
Biometric authentication based on PCG and ECG signals: present status and future directionsAbo-Zahhad et al., 2014
View PDF- Document ID
- 7080341982623349598
- Author
- Abo-Zahhad M
- Ahmed S
- Abbas S
- Publication year
- Publication venue
- Signal, Image and Video Processing
External Links
Snippet
Due to the great advances in biomedical digital signal processing, new biometric traits have showed noticeable improvements in authentication systems. Recently, the ElectroCardioGram (ECG) and the PhonoCardioGraph (PCG) have been proposed as novel …
- 238000000034 method 0 abstract description 33
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Abo-Zahhad et al. | Biometric authentication based on PCG and ECG signals: present status and future directions | |
| Abdeldayem et al. | A novel approach for ECG-based human identification using spectral correlation and deep learning | |
| da Silva Luz et al. | Learning deep off-the-person heart biometrics representations | |
| Singh et al. | Bioelectrical signals as emerging biometrics: Issues and challenges | |
| Wahabi et al. | On evaluating ECG biometric systems: Session-dependence and body posture | |
| Zokaee et al. | Human identification based on ECG and palmprint | |
| Chauhan et al. | A survey of emerging biometric modalities | |
| Sepahvand et al. | A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN | |
| CN106473750A (en) | Personal identification method based on photoplethysmographic optimal period waveform | |
| Matta et al. | Real-time continuous identification system using ECG signals | |
| Gürkan et al. | A novel biometric authentication approach using electrocardiogram signals | |
| Pathoumvanh et al. | Robustness study of ECG biometric identification in heart rate variability conditions | |
| Kuila et al. | Feature extraction of electrocardiogram signal using machine learning classification | |
| Goshvarpour et al. | Human identification using information theory-based indices of ECG characteristic points | |
| Demir et al. | Multi-Layer Co-Occurrence Matrices for Person Identification from ECG Signals. | |
| Sung et al. | ECG authentication in post-exercise situation | |
| Kouchaki et al. | ECG-based personal identification using empirical mode decomposition and Hilbert transform | |
| Zehir et al. | Empirical mode decomposition-based biometric identification using GRU and LSTM deep neural networks on ECG signals | |
| Abdulbaqi et al. | Spoof attacks detection based on authentication of multimodal biometrics face-ECG signals | |
| Pouryayevali | ECG biometrics: new algorithm and multimodal biometric system | |
| Chen et al. | Finger ECG-based authentication for healthcare data security using artificial neural network | |
| Zehir et al. | Involutional neural networks for ECG spectrogram classification and person identification | |
| Tseng et al. | Ecg identification system using neural network with global and local features. | |
| Gurkan et al. | A novel human identification system based on electrocardiogram features | |
| Shahid et al. | A survey on AI-based ECG, PPG, and PCG signals based biometric authentication system |