Earticle

초록

영어

The increasing identity theft cases are alarming which puts biometric as the alternative solution to combat identity crime. Recently, biosignals are proposed as biometric modalities. Thus, in this study, the development of an Acceleration Plethysmogram (APG) based Cardioid graph biometric identification is presented. A total of 10 Photoplethysmogram (PPG) data from MIMIC II Waveform Database (MIMIC2WDB) with sampling frequency of 125 Hz were obtained. The datasets are later converted to APG signal by the second order differentiation and preprocessed with Butterworth filter. Then, Cardioid based graph of APG signal was generated. Its centroid and Euclidean distance are calculated. Finally, classification is done by applying these extracted features to Multilayer Perceptron (MLP) and Naïve Bayes neural networks classifiers. Our experimentation results show that subject recognition is possible by obtaining classification accuracy of 95% for APG based Cardioid graph for both classifiers while only 85% and 70% for PPG signal in MLP and Naïve Bayes classifiers. These outcomes indicate that APG based Cardioid graph biometric identification is a feasible solution to overcome identity fraud.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Methodology
  3.1. Data Collection
  3.2. Signal Differentiation
  3.3. Pre-processing
  3.4. APG Segmentation
  3.5. Domain Transformation
  3.6. Feature Extraction
  3.7. Classification
  3.8. Naïve Bayes
 4. Experimentation and Results
 5. Conclusion
 References

키워드

저자

• Khairul Azami Sidek [ Department of Electrical and Computer Engineering, International Islamic University Malaysia P. O. Box 10, Jalan Gombak, 50728 Kuala Lumpur ]
• Munieroh Osman [ Department of Electrical and Computer Engineering, International Islamic University Malaysia P. O. Box 10, Jalan Gombak, 50728 Kuala Lumpur ]
• Siti Nurfarah Ain Mohd Azam [ Department of Electrical and Computer Engineering, International Islamic University Malaysia P. O. Box 10, Jalan Gombak, 50728 Kuala Lumpur ]
• Nur Izzati Zainal [ Department of Electrical and Computer Engineering, International Islamic University Malaysia P. O. Box 10, Jalan Gombak, 50728 Kuala Lumpur ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605