Hand-dorsa Vein Recognition System is a biometric authentication system using inherent physiological characteristics to enable identification of individuals. Texture Description and classification are important feature analysis methods in hand-dorsa vein Recognition. In this paper a new feature description method such as Completed LBTP (C-LBTP) has been proposed to represent selected features from Hand vein image system. C-LBTP combines the features of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). CLBTP is a texture descriptor used to extract the local information from the input image. To find the efficient patterns from feature vectors, a new efficient classifier based on minimum distance classification is proposed. The classification results are checked with accuracy and reliability. The proposed method is evaluated on a NCUT Dataset contains 2040 images from Prof. Yiding Wang, North China University of technology (NCUT) (Wang et al, 2010). Similarity measures of various classification methods such as Chisquare, Cityblock, Euclidean, Chebychev and Minkowski are computed and compared for the better performance. The experimental results show that the proposed C-LBTP feature descriptor achieved good performance.