006 Spezielle Computerverfahren
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Reconstructing 3D face shape from a single 2D photograph as well as from video is an inherently ill-posed problem with many ambiguities. One way to solve some of the ambiguities is using a 3D face model to aid the task. 3D morphable face models (3DMMs) are amongst the state of the art methods for 3D face reconstruction, or so called 3D model fitting. However, current existing methods have severe limitations, and most of them have not been trialled on in-the-wild data. Current analysis-by- synthesis methods form complex non linear optimisation processes, and optimisers often get stuck in local optima. Further, most existing methods are slow, requiring in the order of minutes to process one photograph.
This thesis presents an algorithm to reconstruct 3D face shape from a single image as well as from sets of images or video frames in real-time. We introduce a solution for linear fitting of a PCA shape identity model and expression blendshapes to 2D facial landmarks. To improve the accuracy of the shape, a fast face contour fitting algorithm is introduced. These different components of the algorithm are run in iteration, resulting in a fast, linear shape-to- landmarks fitting algorithm. The algorithm, specifically designed to fit to landmarks obtained from in-the-wild images, by tackling imaging conditions that occur in in-the-wild images like facial expressions and the mismatch of 2D–3D contour correspondences, achieves the shape reconstruction accuracy of much more complex, nonlinear state of the art methods, while being multiple orders of magnitudes faster.
Second, we address the problem of fitting to sets of multiple images of the same person, as well as monocular video sequences. We extend the proposed shape-to-landmarks fitting to multiple frames by using the knowledge that all images are from the same identity. To recover facial texture, the approach uses texture from the original images, instead of employing the often-used PCA albedo model of a 3DMM. We employ an algorithm that merges texture from multiple frames in real-time based on a weighting of each triangle of the reconstructed shape mesh.
Last, we make the proposed real-time 3D morphable face model fitting algorithm available as open-source software. In contrast to ubiquitous available 2D-based face models and code, there is a general lack of software for 3D morphable face model fitting, hindering a widespread adoption. The library thus constitutes a significant contribution to the community.
A 3D face modelling approach for pose-invariant face recognition in a human-robot environment
(2017)
Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis.
In this paper, we propose a novel fitting method that uses local image features to fit a 3D morphable face model to 2D images. To overcome the obstacle of optimising a cost function that contains a non-differentiable feature extraction operator, we use a learning-based cascaded regression method that learns the gradient direction from data. The method allows to simultaneously solve for shape and pose parameters. Our method is thoroughly evaluated on morphable model generated data and first results on real data are presented. Compared to traditional fitting methods, which use simple raw features like pixel colour or edge maps, local features have been shown to be much more robust against variations in imaging conditions. Our approach is unique in that we are the first to use local features to fit a 3D morphable model. Because of the speed of our method, it is applicable for realtime applications. Our cascaded regression framework is available as an open source library at github.com/patrikhuber/ superviseddescent.