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This paper presents an evaluation of three classifiers using the discrete wavelet transform (DWT) as a feature extractor. The thrust is to investigate the impact of DWT with its various filter banks on the HMM, PCA and SHMM classifiers. In addition, we have developed a novel approach that combines the multiresolution feature of the discrete wavelet transform with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). Tests have been carried out on the AT&T and Essex face databases, which show that DWT/SHMM outperforms both the DWT/HMM and DWT/PCA with an 8% increase in accuracy.