Manifold analysis of facial gestures for face recognition
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Human computer intelligent interaction using augmented cognition and emotional intelligence
ICVR'07 Proceedings of the 2nd international conference on Virtual reality
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In performance-driven facial animation (PDFA), an animated character is driven by the contraction of facial muscles of a performer. Most existing work maps literal skin motion from the performer to the character, resulting in “human like” deformation of the face. This is desirable for realistic human animation, but is unintuitive, difficult to edit, and allows little or no flexibility in re-mapping the resulting animation to a non-human character. manifestation of the contraction of one or more facial muscles. By mapping choose the resulting output and is not constrained by the literal interpretation of facial motion. Extracting these parameters, however, has proved to be much more difficult than traditional motion capture and, as such, the majority of facial parameter extraction work has been performed in the area of facial analysis and not applied to PDFA. In PDFA, the intensities of gestures are essential to produce continuous and subtly varying expressions, but existing facial analysis work focuses only on the presence or absence of a gesture (binary analysis). Though practical to obtain, images of face state are an unnecessarily high dimensional representation for a fairly low degree of freedom phenomenon. To address this problem in the context of facial gesture analysis, in this work, the face is partitioned into local regions of change called coarticulation regions (CR) to constrain the number of muscle degrees of freedom. By reducing the dimensionality of gesture data in each CR with principal component analysis, a coherent low dimensional appearance structure to gesture intensities is uncovered. The structures induced by independent gesture actuations are termed gesture manifolds, or G-Folds. This structure is modeled with quadratic polynomials in Gesture Polynomial Reduction. The continuous G-Fold representation is a fundamental improvement over discrete template based approaches and heuristic models of facial action. The utility of the model is demonstrated by classifying fine levels of gesture intensity and applying the results to a novel 2D animation technique called Muscle Morphing, combining mass-spring muscle control with image warping.