Paper Info
Title
Global feature based female facial beauty decision system
Abstract
This paper presents an automated female facial beauty decision system based on Support Vector Machine (SVM). First, we constructed manually two classes of female faces with respect to their facial beauty, by requesting personal opinions of people. As the second step, Principal Components Analysis (PCA) and Kernel PCA(KPCA) were applied to each class for extracting principal features of beauty. Support Vector Machine (SVM) was used for judging whether a given face is beautiful or not. Since judging the beauty is subjective, the decision results of our system were evaluated by comparing the system generated decision results with the corresponding ones made by the persons. Based on this criteria, our results showed that KPCA with a success ratio of 89% outperformed PCA with a success ratio of 83%.
Year
Venue
Keywords
2007
Poznan
face recognition,feature extraction,principal component analysis,support vector machines,kpca,svm,automated female facial beauty decision system,global feature,kernel pca,principal components analysis,principal feature extraction,support vector machine,decision support systems,signal processing
Field
DocType
ISBN
Signal processing,Pattern recognition,Computer science,Decision support system,Support vector machine,Decision system,Beauty,Kernel principal component analysis,Artificial intelligence,Feature based,Principal component analysis,Machine learning
Conference
978-839-2134-04-6
Citations 
PageRank 
References 
4
0.55
2
Authors
3
Name
Order
Citations
PageRank
H. Irem Türkmen1203.93
Zeyneb Kurt261.93
M. Elif Karsligil37313.69