Face liveness detection aims to distinguish genuine faces from disguised faces. Most previous works under visible light focus on classification of genuine faces and planar photos or videos. To handle the three-dimensional (3-D) disguised faces, liveness detection based on multispectral images has been shown to be an effective choice. In this paper, a gradient-based multispectral method has been proposed for face liveness detection. Three feature vectors are developed to reduce the influence of varying illuminations. The reflectance-based feature achieves the best performance, which has a true positive rate of 98.3% and a true negative rate of 98.7%. The developed methods are also tested on individual bands to provide a clue for band selection in the imaging system. Preliminary results on different face orientations are also shown. The contributions of this paper are threefold. First, a gradient-based multispectral method has been proposed for liveness detection, which considers the reflectance properties of all the distinctive regions in a face. Second, three illumination-robust features are studied based on a dataset with two-dimensional planar photos, 3-D mannequins, and masks. Finally, the performance of the method on different spectral bands and face orientations is also shown in the evaluations.