DocumentCode
700093
Title
Gender determination using a Support Vector Machine Variant
Author
Zafeiriou, Stefanos ; Tefas, Anastasios ; Pitas, Ioannis
Author_Institution
Dept. of Inf., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece
fYear
2008
fDate
25-29 Aug. 2008
Firstpage
1
Lastpage
5
Abstract
In this paper a modified class of Support Vector Machines (SVMs) inspired from the optimization of Fisher´s discriminant ratio is presented. Moreover, we present a novel class of nonlinear decision surfaces by solving the proposed optimization problem in arbitrary Hilbert spaces defined by Mercer´s kernels. The effectiveness of the proposed approach is demonstrated by comparing it with the standard SVMs and other classifiers, like Kernel Fisher Discriminant Analysis (KFDA) in gender determination.
Keywords
Hilbert spaces; feature extraction; human computer interaction; optimisation; pattern classification; support vector machines; Fisher discriminant ratio; Kernel Fisher discriminant analysis; Mercer kernels; arbitrary Hilbert spaces; gender determination; nonlinear decision surfaces; support vector machine variant; Error analysis; Face; Kernel; Optimization; Support vector machines; Training; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Conference, 2008 16th European
Conference_Location
Lausanne
ISSN
2219-5491
Type
conf
Filename
7080625
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