Pose Variant Face Recognition Based On 3D Morphable Model

After thirty years of research, face recognition techniques have been made enormous progress. In well-controlled environments, the face recognition were successful in terms of their recognition performance. However, the pose and illumination variation is still very challenging for face recognition. Our work concentrates on the 3D Morphable Model and recognizing faces from different poses. The main study includes the following contents.In this thesis, we present a method to pose variant face recognition that combines two recent advances: Component-based face recognition and 3D Mophable Model. In this method, 3D components is robust to pose changes and are extracted as the input feature to face recognition. For classification, we combine the local feature and global feature of face and the whole face are used as input to the final classifier ,where each component is verified its weight based on its recognition rate. Comparing with Component-based face recognition in 2D, this method is robust to pose variant.A 3D Morphable Model based on components is present in the thesis. In the process of matching a Morphable Model to image, the Euclidean distance between the reconstructed 3D component image and the input component image is defined as the goal function. Comparing with the whole face 3D Morphable Model, the dimension of each component is low and the scale of the goal function of each component is relatively small, so the optimized result is improved and the 3D component is more precise. The reconstruction result, combined with Two-Layer classification, is better to face recognition.