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Photoplethysmogram Based Biometric Recognition for Twins


  • Department of Electrical and Computer Engineering, Faculty of Engineering, International Islamic University Malaysia, 53100 Kuala Lumpur, Malaysia


This paper presents a photoplethysmogram (PPG) based biometric recognition technique for twins. PPG devices have been widely used due to its advantages such as non-invasive, low cost and small in size which makes it a convenient analytical tool. To the best of our knowledge, little has been set pertaining to biometric recognition for twins using PPG signal. A total of six subjects from three couple of twins were used for experimentation purposes. The signals were processed using a low pass filter to remove unwanted noise. Then, multiple cycle of PPG waveforms were extracted and later, Naive Bayes (NB) and Radial Basis Function (RBF) network classifiers are used to categorize the subjects using the discriminant features. Based on the experimentation results, classification accuracies of 97% and 94% were achieved when using Naive Bayes and RBF network respectively which suggests the capability of our proposed system to identify individuals regardless whether the persons is a twin or not. The outcome also provides complimentary mechanism to detect a person besides using the current existing methods.


Naive Bayes, Photoplethysmogram (PPG), Radial Basis Function (RBF) Network, Systolic and Diastolic.

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  • Jain AK, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology. 2004 Jan; 14(1):4-20.
  • Li J, Zhang L, Guo D, Zhuo S and Sim T. Audio-Visual Twins Database. Biometrics (ICB). 2015; p. 493-500.
  • Kong A, Zhang D and Lu G. A Study of Identical Twins’ Palmprints for Personal Authentication. Springer Science. 2005; 3832:668-74.
  • Roberts SC, Gosling LM, Spector TD, Miller P, Penn DJ and Petrie M. Body odor similarity of non-cohabiting twins. Chemical Senses. 2005; 30(8):651-56.
  • Priya BL and Rani MP. An Efficient Method for Recognizing Identical Twins Using Facial Aspects. International Journal of Advanced Technology in Engineering and Science. April 2015; 3:1.
  • Vijayan V, Bowyer KW, Flynn PJ, Huang D, Chen L, Hansen M, Ocegueda O, Shah SK and Kakadiaris IA. Twins 3D Face Recognition Challenge. Biometrics (IJCB). 2011; p. 1-7.
  • Patil BG and Subbaraman S. SVD-EBP Algorithm for Iris Pattern Recognition. (IJACSA) International Journal of Advanced Computer Science and Applications. 2011; 2(12).
  • Harry Z. The Optimality of Naive Bayes. American Association for Artificial Intelligence. 2004; p. 1-6.
  • John AB. Radial Basis Function Networks: Introduction. Introduction to Neural Networks: Lecture 12, 2004; p. 1-12.


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