Face Recognition with Irregular Region Spin Images
This paper explores how spin images can be constructed using shape-from-shading information and used for the purpose of face recognition. We commence by extracting needle maps from gray-scale images of faces, using a mean needle map to enforce the correct pattern of facial convexity and concavity. Spin images  are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on spin image histograms for an arrangement of image patches. Comparing to our previous spin image approach, the current one has two basic difference: Euclidean distance is replaced by geodesic distance; Irregular face region is applied to better fit face contour. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate, and the recognition precision reaches 93% on CMU PIE sub-database.
Unable to display preview. Download preview PDF.
- 2.Blanz: Automatic face identification system using flexible appearance models. IVC 13(5), 393–401 (1995), citeseer.ist.psu.edu/article/lanitis95automatic.html Google Scholar
- 5.Dijkstra, E.W.: A note on two problems in connexion with graphs. In: Numerische Mathematik, vol. 1, pp. 269–271. Mathematisch Centrum, Amsterdam, The Netherlands (1959)Google Scholar
- 7.Li, Y., Hancock, E.: Face recognition using shading-based curvature attributes. In: International Conference on Pattern Recognition, ICPR, Cambridge, UK (August 2004)Google Scholar
- 9.Sim, T., Kanade, T.: Combining models and exemplars for face recognition: An illuminating example. In: Proceedings of the CVPR 2001 Workshop on Models versus Exemplars in Computer Vision (December 2001)Google Scholar
- 10.Smith, W.A.P., Hancock, E.R.: Recovering facial shape and albedo using a statistical model of surface normal direction. In: Proc. ICCV, pp. 588–595 (2005)Google Scholar
- 11.Turk, M., Pentland, A.: Face recognition using eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (1991)Google Scholar