A Multi-modal 2D + 3D Face Recognition Method with a Novel Local Feature Descriptor

The research on depth map is becoming a focus of image understanding and computer vision. In this paper, depth map is introduced to enhance the performance of face recognition and a novel multi-modal 2D + 3D method is proposed. First of all, we propose a new local feature descriptor called Enhanced Local Mixed Derivative Pattern (ELMDP). Then, this feature descriptor is applied on the 2D intensity image and the depth map respectively. At last the two parts of extracted feature are combined together, multiplied by corresponding confidence weights. Experiments are conducted on 3 sub-databases of Curtin Faces database which contains variations in illumination, expression, pose and disguise. Our proposed method outmatches the other methods on recognition rate and the Receiver Operating Characteristic (ROC) curve is much gentler. All the results demonstrate that the proposed method is quite outstanding and robust.