Face recognition algorithms generally use 2D images for feature extraction and matching. In order to achieve better performance, 3D faces captured via specialized acquisition methods have been used to develop improved algorithms. While such 3D images remain difficult to obtain due to several issues such as cost and accessibility, RGB-D images captured by low cost sensors (e.g. Kinect) are comparatively easier to acquire. This research introduces a novel face recognition algorithm for RGB-D images. The proposed algorithm computes a descriptor based on the entropy of RGB-D faces along with the saliency feature obtained from a 2D face. The probe RGB-D descriptor is used as input to a random decision forest classifier to establish the identity. This research also presents a novel RGB-D face database pertaining to 106 individuals. The experimental results indicate that the RGB-D information obtained by Kinect can be used to achieve improved face recognition performance compared to existing 2D and 3D approaches. © 2013 IEEE.