Probabilistic Morphable Models
Egger B, Schönborn S, Blumer C, Vetter T (2017)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2017
Publisher: Elsevier Inc.
Edited Volumes: Statistical Shape and Deformation Analysis
ISBN: 9780128104941
DOI: 10.1016/B978-0-12-810493-4.00006-7
Abstract
3D Morphable Face Models have been introduced for the analysis of 2D face photographs. The analysis is performed by actively reconstructing the three-dimensional face from the image in an Analysis-by-Synthesis loop, exploring statistical models for shape and appearance. Here we follow a probabilistic approach to acquire a robust and automatic model adaptation.The probabilistic formulation helps to overcome two main limitations of the classical approach. First, Morphable Model adaptation is highly depending on a good initialization. The initial position of landmark points and face pose was given by manual annotation in previous approaches. Our fully probabilistic formulation allows us to integrate unreliable Bottom-Up cues from face and feature point detectors. This integration is superior to the classical feed-forward approach, which is prone to early and possibly wrong decisions. The integration of uncertain Bottom-Up detectors leads to a fully automatic model adaptation process. Second, the probabilistic framework gives us a natural way to handle outliers and occlusions. Face images are recorded in highly unconstrained settings. Often parts of the face are occluded by various objects. Unhandled occlusions can mislead the model adaptation process. The probabilistic interpretation of our model makes possible to detect and segment occluded parts of the image and leads to robust model adaptation.Throughout this chapter we develop a fully probabilistic framework for image interpretation. We start by reformulating the Morphable Model as a probabilistic model in a fully Bayesian framework. Given an image, we search for a posterior distribution of possible image explanations. The integration of Bottom-Up information and the model parameters adaptation is performed using a Data Driven Markov Chain Monte Carlo approach. The face model is extended to be occlusion-aware and explicitly segments the image into face and non-face regions during the model adaptation process. The segmentation and model adaptation is performed in an Expectation-Maximization-style algorithm utilizing a robust illumination estimation method.The presented fully automatic face model adaptation can be used in a wide range of applications like face analysis, face recognition or face image manipulation. Our framework is able to handle images containing strong outliers, occlusions and facial expressions under arbitrary poses and illuminations. Furthermore, the fully probabilistic embedding has the additional advantage that it also delivers the uncertainty of the resulting image interpretation.
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Switzerland (CH)How to cite
APA:
Egger, B., Schönborn, S., Blumer, C., & Vetter, T. (2017). Probabilistic Morphable Models. In Guoyan Zheng, Shuo Li, Gabor Székely (Eds.), Statistical Shape and Deformation Analysis. Elsevier Inc..
MLA:
Egger, Bernhard, et al. "Probabilistic Morphable Models." Statistical Shape and Deformation Analysis. Ed. Guoyan Zheng, Shuo Li, Gabor Székely, Elsevier Inc., 2017.
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