Face model fitting with learned displacement experts and multi-band images
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In computer vision applications, models are often used to gain information about real-world objects. In order to determine model parameters that match the image content, displacement experts serve as an update function to refine initial model parameter estimations. However, building robust displacement experts is a non-trivial task, especially in unconstrained environments. Therefore, we provide the fitting algorithm not only with the original image but with a multi-band image representation that reflects the location of several facial components. To demonstrate its robustness in real-world scenarios, we integrate the Labeled Faces In The Wild database, which consists of images that have been taken outside lab environments.
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