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Albedo estimation from a facial image is crucial for various computer vision tasks, such as 3-D morphable-model fitting, shape recovery, and illumination-invariant face recognition, but the currently available methods do not give good estimation results. Most methods ignore the influence of cast shadows and require a statistical model to obtain facial albedo. This paper describes a method for albedo estimation that makes combined use of image intensity and facial depth information for an image with cast shadows and general unknown light. In order to estimate the albedo map of a face, we formulate the albedo estimation problem as a linear programming problem that minimizes intensity error under the assumption that the surface of the face has constant albedo. Since the solution thus obtained has significant errors in certain parts of the facial image, the albedo estimate needs to be compensated. We minimize the mean square error of albedo under the assumption that the surface normals, which are calculated from the facial depth information, are corrupted with noise. The proposed method is simple and the experimental results show that this method gives better estimates than other methods.