This paper presents a novel approach for recognizing faces in images taken from different illumination, expression, near frontal pose, partially occlusion and time delay. The method is based on one dimensional discrete Hidden Markov Model (1D-DHMM) with new way of extracting observations and using observation sequences. All subjects in the system share only one HMM that is used as a means to weigh a pair of observations. The Haar wavelet transform is applied to face images to reduce the dimension of the observation vectors. The selection of the recognized person is based on the highest score, which is the summation of the likelihoods of all observation sequences extracted from image on both vertical and horizontal dimensions. Our experiment results tested on the AR face database and the CMU PIE face database show that the proposed method outperforms the PCA, LDA, LFA based approaches tested on the same databases.