A RGB-D face recognition approach without confronting the camera

Face recognition research mainly focuses on traditional 2D color images, which is extremely susceptible to be affected by external factors such as various viewpoints and has limited recognition accuracy. In order to achieve improved recognition performance, as well as the 3D face holds more abundant information than 2D, we present a 3D human face recognition algorithm using the Microsoft´s Kinect. The proposed approach integrates the depth data with the RGB data to generate 3D face raw data and then extracts feature points, identifies the target via a two-level cascade classifier. Also, we build a 3D-face database including 16 individuals captured exclusively using Kinect. The experimental results indicate that the introduced algorithm can not only achieve better recognition accuracy in comparison to existing 2D and 3D face recognition algorithms when the probe face is exactly in front of Kinect sensor, but also can increase 9.3% of recognition accuracy compared to the PCA-3D algorithm when it is not confronting the camera.