Stefan Gehlen

Strategies and Benefits of Fusion of 2D and 3D Face Recognition

The extension of 2D image-based face recognition methods with respect to 3D shape information and the fusion of both modalities is one of the main topics in the recent development of facial recognition. In this paper we discuss different strategies and their expected benefit for the fusion of 2D and 3D face recognition. The face recognition grand challenge (FRGC) provides for the first time ever a public benchmark dataset of a suitable size to evaluate the accuracy of both 2D and 3D face recognition. We use this benchmark to evaluate hierarchical graph matching (HGM), an universal approach to 2D and 3D face recognition, and demonstrate the benefit of different fusion strategies. The results show that HGM yields the best results presented at the recent FRGC workshop, that 2D face recognition is significantly more accurate than 3D face recognition and that the fusion of both modalities leads to a further improvement of the 2D results.

Evaluation of implicit 3D modeling for pose-invariant face recognition

In this paper, we describe and evaluate an approach that uses implicit models of facial features to cope with the problem of recognizing faces under varying pose. The underlying recognition process attaches a parameterized model to every enrolled image that allows the parameter controlled transformation of the stored biometric template into miscellaneous poses within a wide range. We also propose a method for accurate automatic landmark localization in conjunction with pose estimation, which is required by the latter approach. The approach is extensible to other problems in the domain of face recognition for instance facial expression. In the experimental section we present an analysis with respect to accuracy and compare the computational effort with the one of a standard approach.