In the field of face recognition, pose variation is one of the significant challenges that affects the recognition performance and has been one of the major obstacles hindering the improvement of the face recognition technology. In this study, affine transformation parameters of side face and full-frontal face patches are obtained by applying the weighted Lucas-Kanade (LK) algorithm. We further propose that an optimal parameter for correcting face pose can be obtained based on the maximum Gabor similarity. Furthermore, the average Gabor similarity acquired from the optimal parameter of each face patch can be considered to be the face recognition weight, improving the recognition rate and enhancing the robustness of the pose invariant face recognition. Finally, the experimental results obtained based on the FERET face database denote that the recognition rate for the image with a pose of 45° can reach up to 97.3%, indicating that the usage of the maximum Gabor similarity as a basis for parameter extraction of the weighted LK algorithm is valid. This method can also handle illumination variations. Considering the average Gabor similarity as the recognition weight will ensure the robust and effective application of this algorithm.