Özet:
In this thesis, we attack the problem of identifying humans from their three dimensional facial characteristics. For this purpose, a complete 3D face recognition system is developed. We divide the whole system into sub-processes. These sub-processes can be categorized as follows: 1) registration, 2) representation of faces, 3) extraction of discriminative features, and 4) fusion of matchers. For each module, we evaluate the state-of-the art methods, and also propose novel ones. For the registration task, we propose to use a generic face model which speeds up the correspondence establishment process. We compare the benefits of rigid and non-rigid registration schemes using a generic face model. In terms of face representation schemes, we implement a diverse range of approaches such as point clouds, curvature-based descriptors, and range images. In relation to these, various feature extraction methods are used to determine the discriminative facial features. We also propose to use local region-based representation schemes which may be advantageous in terms of both dimensionality reduction and for determining invariant regions under several facial variations. Finally, with the realization of diverse 3D face experts, we perform an in-depth analysis of decision-level fusion algorithms. In addition to the evaluation of baseline fusion methods, we propose to use two novel fusion schemes where the first one employs a confidence-aided combination approach, and the second one implements a two-level serial integration method. Recog- nition simulations performed on the 3DRMA and the FRGC databases show that: 1) generic face template-based rigid registration of faces is better than the non-rigid variant, 2) principal curvature directions and surface normals have better discriminative power, 3) representing faces using local patch descriptors can both reduce the feature dimensionality and improve the identification rate, and 4) confidence-assisted fusion rules and serial two-stage fusion schemes have a potential to improve the accuracy when compared to other decision-level fusion rules.
