Dense 3D Face Correspondence

We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles. A deformable model (K3DM) is constructed from the dense corresponded faces and an algorithm is proposed for morphing the K3DM to fit unseen faces. This algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model. We have extensively evaluated the proposed algorithms on synthetic data and real 3D faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using quantitative and qualitative benchmarks. Our algorithm achieved dense correspondences with a mean localisation error of 1.28mm on synthetic faces and detected $14$ anthropometric landmarks on unseen real faces from the FRGCv2 database with 3mm precision. Furthermore, our deformable model fitting algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on Bosphorus database. Our dense model is also able to generalize to unseen datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm

Wavelet-Based Registration of Medical Images.

Registration is the process of spatially aligning two objects and is normally a preprocessing step in most object recognition algorithms. Registration of images and recognition of signatures of objects in images is important for clinical and diagnostic purposes in medicine. Recognizing structure, potential targets for defense purposes and changes in the terrain, from aerial surveillance images and SAR images is the focus of extensive research and development today. Automatic Target Recognition is becoming increasingly important as the defense systems and armament technology move to use smarter munitions. Registration of images is a preprocessing step in any kind of machine vision for robots, object recognition in general, etc. Registration is also important for tuning instruments dealing with images. Most of the available methods of registration today are operator assisted. The state of registration today is more art than science and there are no standards for measuring or validating registration procedures. This dissertation provides a viable method to automatically register images of rigid bodies. It provides a method to register CT and MRI images of the brain. It uses wavelets to determine sharp edges. Wavelets are oscillatory functions with compact support. The Wavelet Modulus Maxima singularides. It also provides a mechanism to characterize the singularities in the images using Lipschitz exponents. This research provides a procedure to register images which is computationally efficient. The algorithms and techniques are general enough to be applicable to other application domains. The discussion in this dissertation includes an introduction to wavelets and time frequency analysis, results on MRI data, a discussion on the limitations, and certain requirements for the procedure to work. This dissertation also tracks the movement of edges across scales when a wavelet algorithm is used and provides a formula for this edge movement. As part of this research a registration classification schematic was developed

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases