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Dense 3D facial shape recovery employing shading and correspondences

Human faces are one of the most frequently captured objects in both videos and photographs due to their fundamental role in communication and social interactions. The variability of this facial imagery makes it difficult to automate the understanding of scenes containing faces under unconstrained conditions. For faces, recovering accurate dense 3D facial shape from images and videos enables much richer understanding of the human face and its interaction with the scene. In this thesis, we seek to extend the work in the area of dense 3D facial shape recovery under challenging unconstrained conditions. There are a wealth of ways to recover 3D shape from images and videos, all of which make specific assumptions about the relationship between the individual images and the construction of the scene. Given this broad selection of methods available, we examine three different scenarios for dense 3D facial shape recovery: i) recovery from a single image, ii) recovery from an unconstrained image collection without any explicit 3D shape priors and iii) recovery from a video sequence. We focus on these three cases and show how facial priors can be introduced to tackle the dense 3D facial surface recovery problem. We propose to investigate the use of shading constraints for dense shape recovery from unconstrained images. Given the challenging nature of these images, the introduction of priors greatly improves performance over the generic shape-from-shading literature. However, the introduction of explicit priors comes with a further problem, that of correspondence. That is, recovering the relationship between pixels in the image and the structure of our model. For this reason, we also investigate the importance of finding dense correspondences between facial images. We show that it is possible to recover plausible dense 3D facial surfaces under a variety of different input conditions.
Date January 2017
CreatorsSnape, Patrick
ContributorsZafeiriou, Stefanos ; Pantic, Maja
PublisherImperial College London
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation

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