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We present a nonparametric facial feature localization method using relative directional information between regularly sampled image segments and facial feature points. Instead of using any iterative parameter optimization technique or search algorithm, our method finds the location of facial feature points by using a weighted concentration of the directional vectors originating from the image segments pointing to the expected facial feature positions. Each directional vector is calculated by linear combination of eigendirectional vectors which are obtained by a principal component analysis of training facial segments in feature space of histogram of oriented gradient (HOG). Our method finds facial feature points very fast and accurately, since it utilizes statistical reasoning from all the training data without need to extract local patterns at the estimated positions of facial features, any iterative parameter optimization algorithm, and any search algorithm. In addition, we can reduce the storage size for the trained model by controlling the energy preserving level of HOG pattern space.

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In this paper we combine methods from the field of computer vision with surface editing techniques to generate animated faces, which are all in full correspondence to each other. The input for our system are synchronized video streams from multiple cameras. The system produces a sequence of triangle meshes with fixed connectivity, representing the dynamics of the captured face. By carfully taking all requirements and characteristics into account we decided for the proposed system design: We deform an initial face template using movements estimated from the video streams. To increase the robustness of the initial reconstruction, we use a morphable model as a shape prior. However using an efficient Surfel Fitting technique, we are still able to precisely capture face shapes not part of the PCA Model. In the deformation stage, we use a 2D mesh-based tracking approach to establish correspondences in time. We then reconstruct image-samples in 3D using the same Surfel Fitting technique, and finally use the reconstructed points to robustly deform the initially reconstructed face.

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