In order to address the problem that Local Phase Quantization (LPQ) method couldn’t discriminate among the sub-patterns based on their different contribution when describing the image feature. A method for face recognition called as Adaptively Weighted Local Phase Quantization (AWLPQ) is proposed. At first, the face images are divided into several sub-images and the feature fetch is based on the LPQ method. And then proposed algorithm employs an adaptively weighting map to weight the sub-patterns based on their information entropy which is defined as the contribution to describe the whole face images. Experiments on the FERET face database show that the proposed method is effective. In addition, in order to solve the problem of high dimension in AWLPQ, Neighbor Preserving Embedding (NPE) is applied for dimension reduction. The experimental results indicate that the method gains both relative robustness and good recognition accuracy.