Most of the face recognition algorithms concentrate on the transformations (like DCT, FFT, etc.) for recognition of face images. These transformations concentrate on the global information of the face images and they miss the local information i.e. the relationship with the neighboring pixels. So, here, we considered the local matching method (local binary pattern) for considering the local information of the face images. For better recognition is obtained by combining the local as well as global information of the face image. So, for effective face recognition system, we combined the local binary patterns with the DCT. In this paper, we proposed a face recognition system with local binary pattern with DCT using doubly truncated multivariate Gaussian mixture model. By using EM algorithm with K-means or hierarchical clustering, the model parameters are estimated. The experimentation is carried with two face image databases, namely, Jawaharlal Nehru Technological University Kakinada (JNTUK) and Yale. The proposed system was found to be efficient compared to the existing system using GMM. The effect of the number of DCT coefficients on the recognition rate is also studied and found efficient recognition rate for 15 DCT coefficients. We also studied the recognition rate by varying the number of training images for each person