Feature Extraction for Biometric Recognition with Photoplethysmography Signals

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dc.authoridBOZKURT, MEHMET RECEP/0000-0003-0673-4454
dc.authoridKAVSAOGLU, Ahmet Resit/0000-0002-4380-9075
dc.contributor.authorKavsaoglu, A. Resit
dc.contributor.authorPolat, Kemal
dc.contributor.authorBozkurt, M. Recep
dc.contributor.authorMuthusamy, Hariharan
dc.date.accessioned2025-03-23T19:48:47Z
dc.date.available2025-03-23T19:48:47Z
dc.date.issued2013
dc.departmentSinop Üniversitesi
dc.description21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
dc.description.abstractPhotoplethysmography (PPG) signals stand out due to features such as readily accessible, high reliability and confidentiality, the ease of use etc. among bio-signals. The feasibility studies carried out on the PPG signals demonstrated that PPG signals contained important features for human recognition and were the availability of biometric identification systems. In this study, twenty new features were extracted from PPG signal as a preliminary study intended to biometric recognition. PPG signals with 10 seconds were recorded from five healthy people using SDPPG (second derivative PPG) data acquisition card. To remove the noise from received raw PPG signals, a FIR low pass filtering with 200 points and 10 Hz cut-off frequency was designed. These twenty new features were obtained from filtered PPG signal and its second derivative. PPG signal with 10 seconds contains eight periods and twenty characteristic features in each person must not change within an individual over a period. This feature symbolizes the consistency in the identification of a person. To test the performance of biometric recognition system, the k-NN (k-nearest neighbor) classifier was used and achieved 95% of recognition success rate using 10-fold cross validation with twenty new features. The obtained results showed that the developed biometric recognition system based on PPG signal were very promising.
dc.identifier.isbn978-1-4673-5563-6
dc.identifier.isbn978-1-4673-5562-9
dc.identifier.issn2165-0608
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/11486/7653
dc.identifier.wosWOS:000325005300408
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherIeee
dc.relation.ispartof2013 21st Signal Processing and Communications Applications Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectBiometrics
dc.subjectPhotoplethysmography (PPG)
dc.subjectIdentification
dc.subjectClassification
dc.subjectDerivatives
dc.subjectFeature Extraction
dc.titleFeature Extraction for Biometric Recognition with Photoplethysmography Signals
dc.typeConference Object

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