Face Recognition is one of the most extensively researched problems in biometrics, and many techniques have been proposed in the literature. While the performance of automated algorithms is close to perfect in constrained environments with controlled illumination, pose, and expression variations, recognition in unconstrained environments is still difficult. To mitigate the effect of some of these challenges, researchers have proposed to utilize 3D images which can encode much more information about the face than 2D images. However, due to sensor cost, 3D face images are expensive to capture. On the other hand, RGB-D images obtained using consumer-level devices such as the Kinect, which provide pseudo-depth data in addition to a visible spectrum color image, have a trade-off between quality and cost. In this chapter, we discuss existing RGB-D face recognition algorithms and present a state-of-the-art algorithm based on extracting discriminatory features using entropy and saliency from RGB-D images. We also present an overview of available RGB-D face datasets along with experimental results and analysis to understand the various facets of RGB-D face recognition. © Springer International Publishing Switzerland 2016.