Identical and fraternal twin recognition using photoplethysmogram signals

This paper elaborates on the recognition of identical and fraternal twins by using photo plethysmogram (PPG) signals as an alternative to current techniques of identifying twins for biometric purposes. Based on our knowledge, the study on PPG based biometric for identical and fraternal twins is und...

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Bibliographic Details
Main Authors: Mohammed Nadzri, NurIzzati, Sidek, Khairul Azami
Format: Article
Language:English
English
Published: Asian Research Publishing Network (ARPN) 2017
Subjects:
Online Access:http://irep.iium.edu.my/56738/
http://irep.iium.edu.my/56738/
http://irep.iium.edu.my/56738/1/jeas_0417_5942.pdf
http://irep.iium.edu.my/56738/7/56738-Identical%20and%20fraternal%20twin%20recognition%20using%20photoplethysmogram%20signals_SCOPUS.pdf
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Summary:This paper elaborates on the recognition of identical and fraternal twins by using photo plethysmogram (PPG) signals as an alternative to current techniques of identifying twins for biometric purposes. Based on our knowledge, the study on PPG based biometric for identical and fraternal twins is under-researched. Thus, this issue will be the main focus of our study. PPG samples of nine subjects consisting of two identical twins and another two fraternal twins were collected for experimentation procedures. Next, a low pass filter was used to remove the noise in the signal. Then, the feature extraction process is performed by selecting unique features of PPG signals from an individuals and later classifying the datasets using Naïve Bayes (NB) and Multilayer Perceptron (MLP). Based on the experimentation results, classification accuracies of 97.2% and 93.5% were achieved from the overall dataset and 97.9% of accuracies were achieved from identical twin while 96.7% and 98.3% were achieved from fraternal twins when using NB and MLP respectively. The output of the study suggests the capability of the proposed system to identify the identical and fraternal twins which can act as a compliment to existing recognition approaches.