Title
Performance Study for Multimodel Client Identification System Using Cardiac and Speech Signals
Abstract
A person's physiological or behavioral characteristic can be used as a biometric and provides automatic identification. There are several advantages of this identification method over the traditional approaches. Overall, biometric techniques can potentially prevent unauthorized access. Unlike the traditional approaches which uses keys, ID, and password, these approaches can be lost, stolen, forged and even forgotten. Biometric systems or pattern recognitions system have been acknowledged by many as a solution to overcome the security problems in this current times. This work looks into the performance of these signals at a frequency samples of 16 kHz. The work was conducted for Client Identification (CID) for 20 clients. The building block for these biometric system is based on MFCC-HMM. The purpose is to evaluate the system based on the performance of training data sets of 30%, 50% and 70%. This work is evaluated using biometric signals of Electrocardiogram (ECG), heart sound (HS) and speech (SP) in order to find the best performance based on the complexity of states and Gaussian. The best CID performance was obtained by SP at 95% for 50% training data at 16 kHz. The worst CID performance was obtained by ECG achieving only 53.21 % for 30% data training.
Year
DOI
Venue
2018
10.1109/ISMICT.2018.8573692
2018 12th International Symposium on Medical Information and Communication Technology (ISMICT)
Keywords
Field
DocType
Electrocardiogram,Hidden Markov Model,Mel-Frequency Cepstral Coeffiecients,Client Identification
Training set,Mel-frequency cepstrum,Identification system,Electronic engineering,Speech recognition,Gaussian,Password,Biometrics,Engineering,Hidden Markov model,Biometric system
Conference
ISSN
ISBN
Citations 
2326-828X
978-1-5386-3390-8
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Hadri Hussain100.34
S. Hussain2479.46
Chee-Ming Ting37213.17
Fuad Noman441.73
M. M. Mohammad500.34
Ahmad Zubaidi Abdul Latif600.68
Osamah Al-Hamdani700.34