Learning gender with support faces
Moghaddam, B.
Ming-Hsuan Yang
Mitsubishi Electr. Res. Labs., Cambridge, MA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 2002
Volume: 24,
Issue: 5
On page(s): 707-711
ISSN: 0162-8828
References Cited: 31
CODEN: ITPIDJ
INSPEC Accession Number: 7265320
Digital Object Identifier: 10.1109/34.1000244
Current Version Published: 2002-08-07
Abstract
Nonlinear support vector machines (SVMs) are investigated for
appearance-based gender classification with low-resolution "thumbnail"
faces processed from 1,755 images from the FERET (FacE REcognition
Technology) face database. The performance of SVMs (3.4% error) is shown
to be superior to traditional pattern classifiers (linear, quadratic,
Fisher linear discriminant, nearest-neighbor) as well as more modern
techniques, such as radial basis function (RBF) classifiers and large
ensemble-RBF networks. Furthermore, the difference in classification
performance with low-resolution "thumbnails" (21×12 pixels) and
the corresponding higher-resolution images (84×48 pixels) was
found to be only 1%, thus demonstrating robustness and stability with
respect to scale and the degree of facial detail
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