Abstract
This paper presents a new method for human recognition using the cepstral information. The proposed method consists in extracting the Linear Frequency Cepstral Coefficients (LFCC) from each heartbeat in the homomorphic domain. Thus, the Hidden Markov Model (HMM) under Hidden Markov Model Toolkit (HTK) is used for electrocardiogram (ECG) classification. To evaluate the performance of the classifier, the number of coefficients and the number of frequency bands are varied. Concerning the HMM topology, the number of Gaussians and states are also varied. The best rate is obtained with 32 coefficients, 24 frequency bands, 1 Gaussian and 5 states. Further, the method is improved by adding dynamic features: the first order delta (?) and energy (E) to the coefficients. The approach is evaluated on 18 healthy signals of the MIT_BIH database. The obtained results reveal which LFCC with energy that make a 33 dimensional feature vector leads to the best human recognition rate which is 99.33%.
Highlights
Biometrics is a secure alternative to traditional methods of identity verification of individuals, such as passwords
According to the obtained results concerning this paper, the study concludes that the cepstral information in the homomorphic domain has given a human recognition rate that is equal 99.33% which remains slightly higher than that temporal information being 99.02%
We presented in this paper, a new personal identification method which used the cepstral information in the homomorphic domain
Summary
Biometrics is a secure alternative to traditional methods of identity verification of individuals, such as passwords. The need for new research which could be difficult to imitate is behind the use of the physiological signals ECG as a biometric characteristic [9]. Biel et al [4] are the earliest researchers who have worked with ECG as a biometric characteristic They have extracted twelve features from each record for human recognition in the time domain. Shen et al [13] have limited their interest in their recognition algorithm, to only a few fiducial points, surrounding the QRS complex An attempt is made to present a new method based on features extraction of ECG without fiducial detection. To improve the identification accuracy, an approach based on cepstral information is introduced It is reflected in the compute of the LFCC from each heart beat of the signal ECG.
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