A human identification system based on Heart sounds and Gaussian Mixture Models

摘要

In this paper, we propose a novel biometric method based on the Heart sounds and the Gaussian Mixture Model (GMM). Heart sounds are trained by GMM to build an identification system. The MFCC Feature extraction algorithm is studied and GMM model is built. The optimal parameters are achieved by varying experimental parameters. The system has an accurate recognition rate up to 100% under the experimental conditions. The results show that the system based on GMM has a better performance than the system based on Vector Quantization (VQ).