Biosignal recognition for patients identification

Abstract

A biometric security system has becoming an important application in client identification and verification system. A conventional biometric system is normally based on unimodal biometric that depends on either behavioral or physiological information for authentication purposes. The behavioral biometric depends on human body biometric signal (such as speech) and biosignal biometric (such as electrocardiogram and phonocardiogram or heart sound). The speech signal is commonly used in a recognition system in biometric, while the electrocardiogram and the heart sound have been used to identify a person's diseases, uniquely related to its cluster. However, the conventional biometric system is liable to spoof attack, which affect the performance of the system. In this paper, a multimodal biometric security system is developed, which is based on biometric signal of electrocardiogram and heart sound. The biosignal data involved in the biometric system initially segmented, with each segment Mel Frequency Cepstral Coeffiecients method is exploited for extracting the features. The Hidden Markov Model is used to model the client and to classify the unknown input with respect to the modal. The recognition system involved training and testing session that is known as Client Identification. In this project, twenty clients are tested with the developed system. The best overall performance for 20 clients at 44 kHz was 93.92% for electrocardiogram train at 70% of the training data however the worst overall performance was also electrocardiogram at an increment of data client of 63 clients at 79.91% for 30% training data. It can be concluded that the difference multimodal biometric has a substantial effect on performance of the biometric system and with the increment of data, even with higher sampling rate, the performance still decreased slightly as predicted.