Rethinking Shape from Shading for Spoofing Detection

Spoofing attacks are critical threats to modern face recognition systems, and most common countermeasures exploit 2D texture features as they are easy to extract and deploy. 3D shape-based methods can substantially improve spoofing prevention, but extracting the 3D shape of the face often requires complex hardware such as a 3D scanner and expensive computation. Motivated by the classical shape-from-shading model, we propose to obtain 3D facial features that can be used to recognize the presence of an actual 3D face, without explicit shape reconstruction. Such shading-based 3D features are extracted highly efficiently from a pair of images captured under different illumination, e.g., two images captured with and without flash. Thus the proposed method provides a rich 3D geometrical representation at negligible computational cost and minimal to none additional hardware. A theoretical analysis is provided to support why such simple 3D features can effectively describe the presence of an actual 3D shape while avoiding complicated calibration steps or hardware setup. Experimental validation shows that the proposed method can produce state-of-the-art spoofing prevention and enhance existing texture-based solutions.