Efficient 3D Face Recognition in Uncontrolled Environment

Face recognition in an uncontrolled environment is challenging as body movement and pose variation can result in missing facial features. In this paper, we tackle this problem by fusing multiple RGB-D images with varying poses. In particular, we develop an efficient pose fusion algorithm that frontalizes the faces and combines the multiple inputs. We then introduce a new 3D registration method based on the unified coordinate system (UCS) to compensate for pose and scale variations and normalize the probe and gallery face. To perform 3D face recognition, we train a Support Vector Machine (SVM) with both 2D color and 3D geometric features. Experimental results on a RGB-D dataset show that our method can achieve a high recognition rate and is robust in the presence of pose and expression variations.