Abstract: An improved face verification algorithm is proposed based on the combination of modular 2DPCA and CSLDA in this paper. Feature extraction of client specific linear discriminant analysis (CSLDA) transforms an image matrix to a vector which causes great dimensionality and computational complexity. Furthermore, the local feature is not considered in CSLDA. Then the new method is studied to avoid the deficiency. The initial features are extracted with the original images which are divided into modular sub-images. The 2DPCA is performed to get the low dimensional features which can be computed conveniently. The local features are extracted efficiently using the proposed new method. Then CSLDA is utilized on the new pattern which is obtained through the modular 2DPCA to extract the final features. Compared with PCA, the discriminant information obtained from the between-class scatter matrix and within-class scatter matrix are included using CSLDA. Moreover, client specific subspace could describe the diversity of the different face better and has more robust discriminant information than the traditional LDA. The experimental results obtained on the facial database XM2VTS using the Lausanne protocol and the facial database ORL using the user-defined protocol show that the verification performance of the new method is superior to that of the primary method CSLDA.