Usually, the first processing step in computer vision systems consists of a spatial convolution with only a few simple filters. Therefore, information is lost of it is not represented explicitly for the following processing steps. This paper proposes a new hierarchical filter scheme that can efficiently synthesize the responses for a large number of specific filters. The scheme is based on steerable filters. It also allows for an efficient on-line adjustment of the trade off between the speed and the accuracy of the filters. We apply this method to the detection of facial keypoints, especially the eye corners. These anatomically defined keypoints exhibit a large variability in their corresponding image structures so that a flexible low level feature extraction is required.

The authors introduce a method for the automatic detection of facial features and characteristic anatomical keypoints. In the application they are aiming at the anatomical landmarks are used to accurately measure facial features. Their approach is essentially bused on a selective search and sequential tracking of characteristic edge and line structures of the facial object to be searched. It integrates model knowledge to guarantee a consistent interpretation of the abundance of local features. The search and the tracking is controlled in each step by interpreting the already derived edge and line information in the context of the whole considered region. For their application, the edge and line detection has to be very precise and flexible. Therefore, they apply a powerful filtering scheme based on steerable filters.

Facial keypoints such as eye corners are important features for a number of different tasks in automatic face processing. The problem is that facial keypoints rather have an anatomical high-level definition than a low-level one. Therefore, they cannot be detected reliably by purely data-driven methods like corner detectors that are only based on the image data of the local neighborhood. In this contribution we introduce a method for the automatic detection of facial keypoints. The method integrates model knowledge to guarantee a consistent interpretation of the abundance of local features. The detection is based on a selective search and sequential tracking of edges controlled by model knowledge. For this, the edge detection has to be very flexible. Therefore, we apply a powerful filtering scheme based on steerable filters.