Parametric Stereo for Multi-pose Face Recognition and 3D-Face Modeling
This paper presents a new method for face modeling and face recognition from a pair of calibrated stereo cameras. In a first step, the algorithm builds a stereo reconstruction of the face by adjusting the global transformation parameters and the shape parameters of a 3D morphable face model. The adjustment of the parameters is such that stereo correspondence between both images is established, i.e. such that the 3D-vertices of the model project on similarly colored pixels in both images. In a second step, the texture information is extracted from the image pair and represented in the texture space of the morphable face model. The resulting shape and texture coefficients form a person specific feature vector and face recognition is performed by comparing query vectors with stored vectors. To validate our algorithm, an extensive image database was built. It consists of stereo-pairs of 70 subjects. For recognition testing, the subjects were recorded under 6 different head directions, ranging from a frontal to a profile view. The face recognition results are very good, with 100% recognition on frontal views and 97% recognition on half-profile views.
KeywordsFace Recognition Recognition Rate Query Vector Texture Computation Stereo Correspondence
Unable to display preview. Download preview PDF.
- 1.Bartlett, M.S., Lades, H.M., Sejnowski, T.J.: Independent component representations for face recognition. Proc. of the SPIE Symposium on Electonic Imaging: Science and Technology, pp. 528–539 (1998)Google Scholar
- 2.Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. PAMI 19(7), 711–720 (1997)Google Scholar
- 4.Beymer, D.: Face recognition under varying pose. Tech. Rep. 1461. MIT AI Lab, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
- 5.Beymer, D.: Vectorizing face images by interleaving shape and texture computations. Tech. Rep. 1537, MIT AI Lab, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
- 6.Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: SIGGRAPH 1999: Proc. of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194 (1999)Google Scholar
- 7.Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Trans. PAMI 25(9), 1063–1074 (2003)Google Scholar
- 9.Dimitrijevic, M., Ilic, S., Fua, P.: Accurate Face Models from Uncalibrated and Ill-Lit Video Sequences. In: IEEE Proc. Int. Conf. CVPR, vol. 2, pp. 1034–1041 (2004)Google Scholar
- 12.Pentland, A., Moghaddam, B., Starner, T.: View-Based and Modular Eigenspaces for Face Recognition. In: Proc. Int. Conf. Computer Vision and Pattern Recognition, pp. 84–91 (1994)Google Scholar
- 13.Shan, Y., Liu, Z., Zhang, Z.: Model-Based Bundle Adjustment with Application to Face Modeling. In: International Conference on Computer Vision, vol. 2, p. 644 (2001)Google Scholar
- 14.Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1) (1991)Google Scholar
- 15.Wiskott, L., Fellous, J.-M., Kruger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. PAMI 19(7), 775–779 (1997)Google Scholar