74 International Journal of Systems Biology and Biomedical Technologies, 1(4), 74-87, October-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Feature Extraction, Gaussian Mixture Model, Heart Sounds, Human Print, Identifcation, Vector Quantization, Verifcation 1. INTRODUCTION The need to identify persons correctly and irrevocably has existed for a very long time. The authorization to enter a building, to open a cupboard, to cross a border, to get money from a bank etc. is always connected to the identity of a person. It is therefore necessary to prove this identity in one way or another. This procedure is called verification. A person claims to be authorized or to have a certain identity, and this must then be verified (Wahid et al., in press). Heart Sounds Human Identifcation and Verifcation Approaches using Vector Quantization and Gaussian Mixture Models Neveen I. Ghali, Faculty of Science, Al-Azhar University, Cairo, Egypt Rasha Wahid, Faculty of Science, Al-Azhar University, Cairo, Egypt Aboul Ella Hassanien, Scientifc Research Group in Egypt (SRGE), Faculty of Computers and Information, Cairo University, Cairo, Egypt ABSTRACT In this paper the possibility of using the human heart sounds as a human print is investigated. To evaluate the performance and the uniqueness of the proposed approach, tests using a high resolution auscultation digital stethoscope are done for nearly 80 heart sound samples. The verifcation approach consists of a robust feature extraction with a specifed confguration in conjunction with Gaussian mixture modeling. The similarity of two samples is estimated by measuring the difference between their negative log-likelihood similarities of the features. The experimental results obtained show that the overall accuracy offered by the employed Gaussian mixture modeling reach up to 85%. The identifcation approach consists of a robust feature extraction with a specifed confguration in conjunction with LBG-VQ. The experimental results obtained show that the overall accuracy offered by the employed LBG-VQ reach up to 88.7%. DOI: 10.4018/ijsbbt.2012100106