Recently, many approaches of face recognition have been proposed due to its broad applications. The generalized low rank approximation of matrices(GLRAM) was proposed in [1], and a necessary condition for the solution of GLRAM was presented in [2]. In addition to all these developments, the Two-Dimensional Principal Component Analysis (2DPCA) model is proposed and proved to be an efficient approach for face recognition [5]. In this paper, we proposed Double Sides 2DPCA algorithm via investigating the 2DPCA algorithm and GLRAM algorithm, experiments showed that the Double Sides 2DPCA’s performance is as good as 2DPCA’s and GLRAM’s. Furthermore, the computation cost of recognition is less than 2DPCA and the computation speed is faster than that for GLRAM. Further, we present a new constructive method for incrementally adding observation to the existing eigen-space model for Double Sides 2DPCA, called incremental doubleside 2DPCA. An explicit formula for such incremental learning is derived. In order to illustrate the effectiveness of the proposed approach, we performed some typical experiments.