Paper Info
Title
SVM-Based Face Recognition Using Genetic Search for Frequency-Feature Subset Selection
Abstract
The goal of this paper is to study if there is a dependency between a selected feature vector at each generation of the genetic algorithm and the resulting fitness. In order to see the relation between these parameters, we first use Discrete Cosine Transforms (DCT) to transform each image as a feature vector (i.e., Frequency Feature Subset (FFS)). A Genetic Algorithm (GA) is then used to select a subset of features from the low-dimensional representation by removing certain DCT coefficients that do not seem to encode important information about recognition task. When using SVM, two problems are confronted: how to choose the optimal input feature subset for SVM, and how to set the best kernel parameters. Therefore, obtaining the optimal feature subset and SVM parameters must occur simultaneously. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem.
Year
DOI
Venue
2008
10.1007/978-3-540-69905-7_37
ICISP
Keywords
Field
DocType
feature selection,genetic algorithm approach,genetic search,frequency feature subset,optimal input feature subset,genetic algorithm,certain dct coefficient,selected feature vector,svm-based face recognition,frequency-feature subset selection,svm parameter,feature vector,optimal feature subset,discrete cosine transform,face recognition,support vector machine,genetics
Kernel (linear algebra),Facial recognition system,Feature vector,Pattern recognition,Feature selection,Computer science,Feature (computer vision),Support vector machine,Discrete cosine transform,Artificial intelligence,Genetic algorithm
Conference
Volume
ISSN
Citations 
5099
0302-9743
4
PageRank 
References 
Authors
0.51
10
3
Name
Order
Citations
PageRank
Aouatif Amine1859.29
Mohammed Rziza28918.32
Driss Aboutajdine358988.82