Face Recognition Using Deformable Matching

Summary

We describe flexible images, a new method for modeling images as defomable intensity (or gray) surfaces. The technique simultaneously incorporates the shape (x,y) and texture (I(x,y)) components of the image. Specifically, the intensity surface is modeled as a deformable 3D mesh in (x,y,I(x,y)) space which obeys Lagrangian dynamics. Using an efficient technique for matching two surfaces (in terms of the analytic modes of vibration), we can obtain a dense correspondence field (or 3D warp) between two images. Furthermore, we use explicit statistical learning of the class of valid deformations in order to provide a priori knowledge about object-specific deformations. The resulting formulation leads to a compact representation based on both the physically-based modes of deformation as well as the statistical modes of variation observed in actual training data. We demonstrate the power of this approach with experiments with image matching, interpolation of missing data, and image retrieval in a large face database.