Joint Correspondence Computation and Statistical Analysis of Geometric Models of Human Faces and Bodies

• Applicant: Professor Dr. Joachim Weickert, since 2/2015
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2014 to 2016
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 255664445

This proposal addresses the problem of processing 3-dimensional geometric human face and body models, which can be acquired using a variety of techniques from laser-range scanners to structured light scanners and image-based systems. Two key problems when processing geometric data are the correspondence computation, which identifies intrinsically corresponding parts between two or more geometric models, and the statistical analysis of a population of geometric models, which computes a probability distribution of the geometric models. These two problems are interdependent. One the one hand, the statistical analysis of a population of models requires correspondence information. On the other hand, the probability distribution computed using shape analysis can be used to compute correspondence information robustly and efficiently.In this project, we aim to use this interdependence to solve the two problems jointly. Our goal is to focus on multilinear probability distributions, which can be used to analyze different modes of variation caused by different geometric variations. For instance, this model can be used to statistically analyze shapes of human faces of different subjects with different facial expressions or shapes of human bodies of different subjects in different poses. We expect to find correspondences of significantly higher quality than existing methods by solving the two problems jointly.We plan to address this problem in two steps. First, we focus on fundamental problems, such as the analysis of different tensor decompositions that can be used to compute a multilinear model and the use of such tensor decompositions in a framework that optimizes correspondences. Second, we focus on applying the developed methods to raw data scans. The main challenge of this step is the development of algorithms that are robust with respect to noise and missing data.The knowledge gained using the developed methods can be used in various applications, such as the reconstruction of 3D human face and body models or the recognition of geometric objects. Problems of this type are encountered in areas besides computer vision and computer graphics, for instance in medical and biological applications.

• DFG Programme: Research Grants
• Ehemalige Antragstellerin: Stefanie Wuhrer, Ph.D., until 1/2015