Hierarchical filtering scheme for the detection of facial keypoints

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 efficie...

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Bibliographic Details
Main Authors: Michaelis, M., Herpers, R., Witta, L., Sommer, G.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Biomedical informatics
Computer science
Computer science
control theory
systems
Computer vision
Convolution
Exact sciences and technology
Face detection
Face recognition
Feature extraction
Filtering
Finite impulse response filter
Image processing
Information, signal and communications theory
Kernel
Pattern recognition. Digital image processing. Computational geometry
Signal processing
Telecommunications and information theory
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Summary: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.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1997.595306