Automatic 3D Face Reconstruction based on Single 2D Image

Face recognition combines a number of advantages as it offers speed and reliability of detection at distance from the subject, which makes its candidacy as a biometric feature a fertile area of pattern recognition and image processing research. In general, changes to head pose and facial expression cause a 2D image of a face to become an unreliable form of identification, while a 3D representation suggests all vertices of the 3D facial model as candidate and useful features for face classification and recognition. Established 3D face reconstruction, however, requires multiple 2D images of a subject at different angles but such images are not always available, and proposals to construct a 3D facial model from a single 2D image of the face are hampered by severe restrictions on that image (frontal pose, neutral expression). This paper offers a new proposal to overcome deficiencies of 3D facial model reconstruction from one image, by improvements using an affine transformation and a 3D statistical face model. The experimental results that are presented for these new algorithms indicate that they are efficient and that this method is promising.