Human Recognition System using Cepstral Information

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%.

@article{RABHI2014,
  title     = {Human Recognition System using Cepstral Information},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {4},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Emna RABHI and Zied Lachiri},
  doi       = {10.14569/IJACSA.2014.050431},
  url       = {https://doi.org/10.14569/IJACSA.2014.050431}
}