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.
Hidden markov models for recognition using artificial neural networks / Bevilacqua, Vitoantonio; Mastronardi, Giuseppe; Pedone, A.; Romanazzi, G.; Daleno, D.. - 4113:(2006), pp. 126-134. (Intervento presentato al convegno International Conference on Intelligent Computing, ICIC 2006 tenutosi a Kunming, China nel August 16-19, 2006) [10.1007/11816157_13].
Hidden markov models for recognition using artificial neural networks
BEVILACQUA, Vitoantonio;MASTRONARDI, Giuseppe;
2006-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
