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Hidden Markov Models for Recognition Using Artificial Neural Networks

  • V. Bevilacqua
  • G. Mastronardi
  • A. Pedone
  • G. Romanazzi
  • D. Daleno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

In this paper we use a novel neural approach for face recognition with Hidden Markov Models. A method based on the extraction of 2D-DCT feature vectors is described, and the recognition results are compared with a new face recognition approach with Artificial Neural Networks (ANN). ANNs are used to compress a bitmap image in order to represent it with a number of coefficients that is smaller than the total number of pixels. To train HMM has been used the Hidden Markov Model Toolkit v3.3 (HTK), designed by Steve Young from the Cambridge University Engineering Department. However, HTK is able to speakers recognition, for this reason we have realized a special adjustment to use HTK for face identification.

Keywords

Artificial Neural Network Hide Markov Model Face Recognition Face Image Speaker Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • V. Bevilacqua
    • 1
  • G. Mastronardi
    • 1
  • A. Pedone
    • 1
  • G. Romanazzi
    • 1
  • D. Daleno
    • 1
  1. 1.Dipartimento di Elettrotecnica ed ElettronicaPolytechnic of BariBariItaly

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