A biometric recognition system through photoplethysmogram (PPG) signals
The development of a biometric recognition system using photoplethysmogram (PPG) signal is presented in this study. Empirical mode decomposition (EMD) and power spectral density (PSD) of the PPG signals were tested for performance as the biometric traits. K-nearest neighbors algorithm (KNN), support...
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2019
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_masteral/7033 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | The development of a biometric recognition system using photoplethysmogram (PPG) signal is presented in this study. Empirical mode decomposition (EMD) and power spectral density (PSD) of the PPG signals were tested for performance as the biometric traits. K-nearest neighbors algorithm (KNN), support vector machine (SVM), and random forest (RF) were the primary classifiers tested. An algorithm was made to train, test, and k-fold cross-validate data both from public and local database. Trained data was also used for live testing. The system was able to acquire PPG data of a user using Contec CMS 50D+ pulse oximeter and store the data to a desktop using Python. A graphical user interface was made to allow two main functions, which are enrollment and recognition.Results from data using public database, local database, and live testing showed varying performances The system is less accurate in recognizing live data. However, it produced positive performance when tested on previously stored data from public and local database. It can then be concluded that PPG can be used for biometric recognition system and the weaknesses of the produced system may be addressed through gathering and training with of larger sets of data. |
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