@misc{leRoux, type = {Master Thesis}, author = {le Roux, Gabriel}, title = {Development and Evaluation of Algorithms for 3D Facial Motion Estimation from Stereo Videos}, pages = {52}, language = {en} } @misc{GreweLeRouxPilzetal., author = {Grewe, Carl Martin and Le Roux, Gabriel and Pilz, Sven-Kristofer and Zachow, Stefan}, title = {Spotting the Details: The Various Facets of Facial Expressions}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-67696}, abstract = {3D Morphable Models (MM) are a popular tool for analysis and synthesis of facial expressions. They represent plausible variations in facial shape and appearance within a low-dimensional parameter space. Fitted to a face scan, the model's parameters compactly encode its expression patterns. This expression code can be used, for instance, as a feature in automatic facial expression recognition. For accurate classification, an MM that can adequately represent the various characteristic facets and variants of each expression is necessary. Currently available MMs are limited in the diversity of expression patterns. We present a novel high-quality Facial Expression Morphable Model built from a large-scale face database as a tool for expression analysis and synthesis. Establishment of accurate dense correspondence, up to finest skin features, enables a detailed statistical analysis of facial expressions. Various characteristic shape patterns are identified for each expression. The results of our analysis give rise to a new facial expression code. We demonstrate the advantages of such a code for the automatic recognition of expressions, and compare the accuracy of our classifier to state-of-the-art.}, language = {en} } @inproceedings{GreweleRouxPilzetal., author = {Grewe, Carl Martin and le Roux, Gabriel and Pilz, Sven-Kristofer and Zachow, Stefan}, title = {Spotting the Details: The Various Facets of Facial Expressions}, series = {IEEE International Conference on Automatic Face and Gesture Recognition}, booktitle = {IEEE International Conference on Automatic Face and Gesture Recognition}, doi = {10.1109/FG.2018.00049}, pages = {286 -- 293}, language = {en} }