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.

[1]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[2]  Behrooz Kamgar-Parsi,et al.  Face Recognition with 3D Model-Based Synthesis , 2004, ICBA.

[3]  A-Nasser Ansari,et al.  3D face modeling using two orthogonal views and a generic face model , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[4]  Kenichi Kanatani,et al.  Statistical Optimization for 3-D Reconstruction from a Single View , 2005, MVA.

[5]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[6]  P J. Phillips,et al.  Face Recognition Vendor Test 2000: Evaluation Report , 2001 .

[7]  Lijun Yin,et al.  3D face recognition based on high-resolution 3D face modeling from frontal and profile views , 2003, WBMA '03.

[8]  Pong C. Yuen,et al.  Recognition of head-and-shoulder face image using virtual frontal-view image , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[9]  David J. Kriegman,et al.  Nine points of light: acquiring subspaces for face recognition under variable lighting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Ahmet M. Kondoz,et al.  Automatic 3D face synthesis using single 2D video frame , 2004 .

[11]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[12]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[13]  Yuxiao Hu,et al.  Automatic 3D reconstruction for face recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[14]  I. Reid,et al.  Single view metrology , 1999, ICCV 1999.

[15]  Charles Beumier.  3D Face Recognition , 2006 .

[16]  Rama Chellappa,et al.  Illumination-insensitive face recognition using symmetric shape-from-shading , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).