In this paper, we introduce a face recognition algorithm based on doubly truncated multivariate Gaussian mixture model with Discrete Cosine Transform (DCT) and Local binary pattern (LBP). Here, the input face image is transformed to the local binary pattern domain. The obtained local binary pattern image is divided into non-overlapping blocks. Then from each block the DCT coefficients are computed and feature vector is extracted. Assigning that the feature vector follows a doubly truncated multivariate Gaussian mixture distribution, the face image is modelled. By using the Expectation-Maximization algorithm the model parameters are estimated. The initialization of the model parameters is done by using either K-means algorithm or hierarchical clustering algorithm and moment method of estimation. The face recognition system is developed with the likelihood function under Bayesian frame. The efficiency of the developed face recognition system is evaluated by conducting experimentation with JNTUK and Yale face image databases. The performance measures like half total error rate, recognition rates are computed along with plotting the ROC curves. A comparative study of the developed algorithm with some of the earlier existing algorithm revealed that this system perform better since, it utilizes local and global information of the face.