Face pose discrimination using support vector machines (SVM)
Huang, J.
Shao, X.
Wechsler, H.
Dept. of Comput. Sci., George Mason Univ., Fairfax, VA;
This paper appears in: Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Publication Date: 16-20 Aug 1998
Volume: 1,
On page(s): 154-156 vol.1
Meeting Date: 08/16/1998 - 08/20/1998
Location: Brisbane, Qld., Australia
ISBN: 0-8186-8512-3
References Cited: 7
INSPEC Accession Number: 6103975
Digital Object Identifier: 10.1109/ICPR.1998.711102
Current Version Published: 2002-08-06
Abstract
This paper describes an approach for the problem of face pose
discrimination using support vector machines (SVM). Face pose
discrimination means that one can label the face image as one of several
known poses. Face images are drawn from the standard FERET database. The
training set consists of 150 images equally distributed among frontal,
approximately 33.75° rotated left and right poses, respectively, and
the test set consists of 450 images again equally distributed among the
three different types of poses. SVM achieved perfect
accuracy-100%-discriminating between the three possible face poses on
unseen test data, using either polynomials of degree 3 or radial basis
functions (RBF) as kernel approximation functions
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
