NDLI: Real-time facial feature detection using conditional regression forests

Content Provider	IEEE Xplore Digital Library
Author	Dantone, M. Gall, J. Fanelli, G. Van Gool, L.
Copyright Year	2012
Description	Author affiliation: ETH Zurich, Switzerland (Dantone, M.; Gall, J.; Fanelli, G.; Van Gool, L.)
Abstract	Although facial feature detection from 2D images is a well-studied field, there is a lack of real-time methods that estimate feature points even on low quality images. Here we propose conditional regression forest for this task. While regression forest learn the relations between facial image patches and the location of feature points from the entire set of faces, conditional regression forest learn the relations conditional to global face properties. In our experiments, we use the head pose as a global property and demonstrate that conditional regression forests outperform regression forests for facial feature detection. We have evaluated the method on the challenging Labeled Faces in the Wild [20] database where close-to-human accuracy is achieved while processing images in real-time.
Starting Page	2578
Ending Page	2585
File Size	905029
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247976
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Facial features Vegetation Training Head Real time systems Databases Accuracy
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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