Face detection with boosted Gaussian features

Detecting faces in images is a key step in numerous computer vision applications, such as face recognition or facial expression analysis. Automatic face detection is a difficult task because of the large face intra-class variability which is due to the important influence of the environmental condit...

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Bibliographic Details
Published in:Pattern recognition 2007-08, Vol.40 (8), p.2283-2291
Main Authors: Meynet, Julien, Popovici, Vlad, Thiran, Jean-Philippe
Format: Article
Language:English
Subjects:
AdaBoost
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Face detection
Gaussian features
Image processing
Information, signal and communications theory
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Signal processing
Telecommunications and information theory
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Summary:Detecting faces in images is a key step in numerous computer vision applications, such as face recognition or facial expression analysis. Automatic face detection is a difficult task because of the large face intra-class variability which is due to the important influence of the environmental conditions on the face appearance. We propose new features based on anisotropic Gaussian filters for detecting frontal faces in complex images. The performances of our face detector based on these new features have been evaluated on reference test sets, and clearly show improvements compared to the state-of-the-art.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2007.02.001