Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Facial Feature Detection and Tracking with Automatic Template Selection
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Initial pose estimation for 3D model tracking using learned objective functions
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Facial expression recognition for human-robot interaction: a prototype
RobVis'08 Proceedings of the 2nd international conference on Robot vision
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Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge is to determine the model parameters that best match a given image by searching for the global optimum of the involved objective function. Unfortunately, this function is usually designed manually, based on implicit and domain-dependent knowledge, which prevents the fitting task from yielding accurate results. In this paper, we demonstrate how to improve model fitting by learning objective functions from annotated training images. Our approach automates many critical decisions and the remaining manual steps hardly require domain-dependent knowledge. This yields more robust objective functions that are able to achieve the accurate model fit. Our evaluation uses a publicly available image database and compares the obtained results to a recent state-of-the-art approach.