One-Sample Face Recognition Using HMM Model of Fiducial Areas

In most real world applications, multiple image samples of individuals are not easy to collate forrecognition or verification. Therefore, there is a need to perform these tasks even if only onetraining sample per person is available. This paper describes an effective algorithm forrecognition and verification with one sample image per class. It uses two dimensional discretewavelet 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&Tdatabase and up to 90% correct classification (Hit) and false acceptance rate (FAR) of 0.02%was achieved.