Global feature based female facial beauty decision system

H. Irem Türkmen; Zeyneb Kurt; M. Elif Karsligil

Global feature based female facial beauty decision system

H. Irem Türkmen^*, Zeyneb Kurt, M. Elif Karsligil

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

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%.

Original language	English
Title of host publication	15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings
Pages	1945-1949
Number of pages	5
Publication status	Published - 1 Dec 2007
Externally published	Yes
Event	15th European Signal Processing Conference, EUSIPCO 2007 - Poznan, Poland Duration: 3 Sept 2007 → 7 Sept 2007

Publication series

Name	European Signal Processing Conference
ISSN (Print)	2219-5491

Conference

Conference	15th European Signal Processing Conference, EUSIPCO 2007
Country/Territory	Poland
City	Poznan
Period	3/09/07 → 7/09/07

Cite this

@inproceedings{0a14617265bb4942ade60daee64ff332,

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%.",

author = "T{\"u}rkmen, {H. Irem} and Zeyneb Kurt and Karsligil, {M. Elif}",

year = "2007",

month = dec,

day = "1",

language = "English",

isbn = "9788392134022",

series = "European Signal Processing Conference",

pages = "1945--1949",

booktitle = "15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings",

note = "15th European Signal Processing Conference, EUSIPCO 2007 ; Conference date: 03-09-2007 Through 07-09-2007",

}

TY - GEN

T1 - Global feature based female facial beauty decision system

AU - Türkmen, H. Irem

AU - Kurt, Zeyneb

AU - Karsligil, M. Elif

PY - 2007/12/1

Y1 - 2007/12/1

N2 - 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%.

AB - 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%.

UR - http://www.scopus.com/inward/record.url?scp=84863766446&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84863766446

SN - 9788392134022

T3 - European Signal Processing Conference

SP - 1945

EP - 1949

BT - 15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings

T2 - 15th European Signal Processing Conference, EUSIPCO 2007

Y2 - 3 September 2007 through 7 September 2007

ER -

Global feature based female facial beauty decision system

Abstract

Publication series

Conference

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