RGB-D Face Recognition With Texture and Attribute Features

Face recognition algorithms generally utilize 2D images for feature extraction and matching. To achieve higher resilience toward covariates, such as expression, illumination, and pose, 3D face recognition algorithms are developed. While it is challenging to use specialized 3D sensors due to high cost, RGB-D images can be captured by low-cost sensors such as Kinect. This research introduces a novel face recognition algorithm using 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. Geometric facial attributes are also extracted from the depth image and face recognition is performed by fusing both the descriptor and attribute match scores. The experimental results indicate that the proposed algorithm achieves high face recognition accuracy on RGB-D images obtained using Kinect compared with existing 2D and 3D approaches.