Face Recognition Using 3D Face Rectification

While face recognition algorithms have shown promising results using gray level face images, their accuracy deteriorate if the face images are not frontal. As the head can move freely, it causes a key challenge in the problem of face recognition. The challenge is how to automatically and without manual intervention recognize nonfrontal face images in a gallery with frontal face images. The rotation is a linear problem in 3D space and can be solved easily using the 3D face data. However, the recognition algorithms based on 3D face data gain less recognition rates than the methods based on 2D gray level images. In this chapter, a sequential algorithm is proposed which uses the benefits of both 2D and 3D face data in order to obtain a pose invariant face recognition system. In the first phase, facial features are detected and the face pose is estimated. Then, the 3D data (Face depth data) and correspondingly the 2D image (Gray level face data) are rotated in order to obtain a frontal face image. Finally, features are extracted from the frontal gray level images and used for classification. Experimental results on FRAV3D face database show that the proposed method can drastically improve the recognition accuracy of non-frontal face images.

Keywords: 3D rotation, Biometric, Depth data, Dimensionality reduction, Ellipse fitting, Eigen problem, Eigenface, Face recognition, Facial features, Fisherface, Feature extraction, Gray level image, IRAD contours, Linear Discriminant Analysis, Least mean square, Manifold learning, Mean filter, Mean curvature, Nearest Neighbor classifier, Pose estimation.