NDLI: Invariant face detection with support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Terrillon, T.J. Shirazi, M.N. Sadek, M. Fukamachi, H. Akamatsu, S.
Copyright Year	2000
Description	Author affiliation: ATR Human Inf. Process. Res. Lab., Japan (Terrillon, T.J.)
Abstract	This paper present an analysis of the performance of support vector machines (SVMs) for automatic detection of human faces in static color images of complex scenes. Skin color-based image segmentations initially performed for several different chrominance spaces by use of the single Gaussian chrominance model and a Gaussian mixture density model. Feature extraction in the segmented images is then implemented by use of invariant orthogonal Fourier-Mellin moments. For all chrominance spaces, the application of SVMs to the invariant moments obtained from a set of 100 test images yields a higher face detection performance than when applying a 3-layer perceptron neural network (NN), depending on a suitable selection of the kernel function used to train the SVM and of the value of its associated parameter(s). The training of SVMs is easier and faster than that of a NN, always finds a global minimum, and SVMs have a better generalization ability.
Starting Page	210
Ending Page	217
File Size	833984
Page Count	8
File Format	PDF
ISBN	0769507506
ISSN	10514651
DOI	10.1109/ICPR.2000.902897
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-03
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face detection Support vector machines Neural networks Image segmentation Image analysis Image color analysis Performance analysis Humans Layout Skin
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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