In most real world applications, multiple image samples of individuals are not easy to collate for recognition or verification. Therefore, there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images; and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90 % correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved
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