Herein, a face recognition algorithm based on an adaptive weighted Curvelet gradient direction histogram is proposed. First, the Curvelet transform based Wrapping is used to extract facial features with multi-orientations, and the coding method is exploited to fuse the original Curvelet features that have the same scale. Second, the fused image is divided into numerous equal-sized non-overlapping rectangular blocks. The face image is then described using the histogram sequence extracted from all the blocks using the HOG operator, and the adaptive weighting of histograms with each scale is separately performed. Finally, the extracted features are fed into the nearest neighbor-based classifier. Results of the simulation experiments conducted using the ORL, YALE, and CAS-PEAL face databases show that the proposed algorithm has a high face recognition rate and good robustness under the influence of interference factors such as face occlusion, gesture transformation, expression transformation, and illumination transformation.