Human identification using heart sound

In this paper, we investigate the possibility of using heart sound as a biometric for human identification. The most significant contribution of using heart sound as a biometric is that it cannot be easily simulated or replicated as compared to other conventional biometrics. Our approach consists of a robust feature extraction scheme which is based on cepstral analysis with a specified configuration, combined with Gaussian mixture modeling. Various experiments have been conducted to determine the relationship between various parameters in our proposed scheme. The results suggest that parameter values appropriate for heart sounds should be significantly different for equivalent parameters used in conventional cepstral analysis for speech processing. In particular, heart sounds should be processed within segments of 0.5 second and using the full resolution in frequency domain. Secondly, higher order cepstral coefficients, carrying information on the excitation, are also useful. Preliminary results indicate that with well-chosen parameters, an identification rate of up to 96% is achievable for a database consisting of 7 individuals, with heart sounds collected over a period of 2 months.