Presentation attack detection is a challenging problem that aims at exposing an impostor user seeking to deceive the authentication system. In facial biometrics systems, this kind of attack is performed using a photograph, video, or 3D mask containing the biometric information of a genuine identity. In this paper, we propose a novel approach to detecting face presentation attacks based on intrinsic properties of the scene such as albedo, depth, and reflectance properties of the facial surfaces, which were recovered through a shape-from-shading (SfS) algorithm. To extract meaningful patterns from the different maps obtained with the SfS algorithm, we designed a novel shallow CNN architecture for learning features useful to the presentation attack detection (PAD). We performed several experiments considering the intra- and inter-dataset evaluation protocols. The obtained results showed the effectiveness of the proposed method considering several types of photo- and video-based presentation attacks, and in the cross-sensor scenario, besides achieving competitive results for the inter-dataset evaluation protocol.