It is observed that only certain portions of the face images that are affected due to expressions, non uniform lighting and partial occlusions are responsible for the failure of face recognition. A methodology of identifying and reducing the influence of such regions in the recognition process is proposed in this paper. Dense correspondence is established between the probe image and a template face-model using optical flow technique. The face image is divided into modules and the summation of the magnitudes of the flow vectors in each module are used in determining the effectiveness of that module in the overall recognition. A low weightage is assigned to the modules whose summation of magnitudes of the flow vectors within that module is high and vice versa. An eye center location algorithm based on adaptive thresholding is implemented to align the test image with the face model prior to establishing the correspondence. Recognition accuracy has increased considerably for PCA based linear subspace approaches when implemented along with the proposed technique.