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Published on: April 6, 2020
Heartbeat detection and personal authentication using a 60 GHz Doppler sensor
Takuma Asano1, Shintaro Izumi1, Hiroshi Kawaguchi1
1Architecture Laboratory, Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan.
This study introduces non-contact biometric authentication using microwave Doppler sensors to analyze heartbeat signals. The method achieves high accuracy for authentication and identification, enhancing security without privacy concerns.
Area of Science:
- Biometrics
- Signal Processing
- Machine Learning
Background:
- Microwave Doppler sensors measure physiological movements for biometric data.
- Non-contact sensing offers privacy advantages over traditional biometrics.
- This study explores non-contact seismocardiography for biometric authentication.
Purpose of the Study:
- To develop and validate a non-contact heartbeat-based biometric authentication system.
- To apply microwave Doppler sensing and machine learning for secure identification.
- To address privacy concerns associated with current biometric technologies.
Main Methods:
- Utilized a 60 GHz microwave Doppler sensor to capture heartbeat signals.
- Employed a conformer network for signal processing and feature extraction.
- Applied a conditional variational autoencoder (CVAE) for subject-specific feature encoding and authentication.
Main Results:
- Achieved an average balanced accuracy of 97.3% for authentication.
- Attained an average accuracy of 94.7% for identification across 13 subjects.
- Demonstrated superior performance in encoding subject-specific features and mitigating noise.
Conclusions:
- The proposed method enhances the feasibility of non-contact, privacy-preserving biometric authentication.
- High accuracy was achieved despite noise challenges, improving biometric security.
- Future work should address posture variations and scalability for real-world applications.
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