Gabor features have been used widely in face identification because of their good results and robustness. However, face identification is strongly affected when the test images are very different from those of the gallery, as is the case in varying face pose. In this paper, a new 2D Gabor-based method is proposed that modifies the grid from which the Gabor features are extracted using a mesh to model face deformations produced by varying pose. Also the Gabor filter kernels are modified by the deformation field computed using the active shape model adjusted to the face. Therefore, the position of the Gabor jets with respect to the face features (eyes, nose, mouth, etc.) is closer to the original position in the frontal face and the modified Gabor filter kernel adjusts to the varying pose. Our method incorporates blocks for illumination compensation by a Local Normalization method, and entropy-weighted Gabor features to emphasize those features that yield proper identification. The method was assessed on the FERET database including pose variations for +/- 60 degrees, +/- 40 degrees, +/- 25 degrees, +/- 15 degrees, and 0 degrees. We compared our results to those previously published for 2D methods. Our proposed method achieved the highest classification accuracy on the FERET database.