Hybrid Hidden Markov Models and Neural Networks Based on Face Recognition

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In this paper, the basic principles of HMM, HMM studied three major issues need to be addressed as well as overflow problems in the practical application of how to solve the HMM. Because artificial neural network (ANN) with anti-noise, adaptive, learning ability, recognition speed, etc., taking into account the characteristics of the common features of speech recognition and pattern recognition and artificial neural networks have, this article will get a mixed combination of HMM in ANN model, using ANN to make up for some deficiencies of HMM. Experiments show that the hybrid model recognition rate than the HMM model increased by 4%, but the algorithm still has many defects to be resolved.

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Periodical:

Edited by:

Dashnor Hoxha, Francisco E. Rivera and Ian McAndrew

Pages:

781-784

Citation:

M. Q. Wang et al., "Hybrid Hidden Markov Models and Neural Networks Based on Face Recognition", Advanced Materials Research, Vol. 1016, pp. 781-784, 2014

Online since:

August 2014

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$38.00

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