DocumentCode :
152755
Title :
Gender classification with Local Zernike Moments and local binary patterns
Author :
Coban, Bahtiyar Samet ; Gokmen, Muhittin
fYear :
2014
fDate :
23-25 April 2014
Firstpage :
1475
Lastpage :
1478
Abstract :
This study provides a new feature extraction method to gender classification. Local Zernike Moments is a method used for face recognition and proved that it is more successful than Gabor or LBP representations. In this study, LZM method is used for gender classification on FERET and LFW databases and demonstrated that it is more successful than LBP method on both databases. In the light of analysis done on the test results of these two methods, a new hybrid feature method built by combining LZM and LBP features is created and the performance rates are achieved as 99.57% for FERET and 97.71% for LFW databases by using Support Vector Machines (SVM) classifier. This indicates the superiority of the proposed method over suggested methods for gender classification on both controlled environment and real-world images.
Keywords :
Zernike polynomials; face recognition; feature extraction; gender issues; image classification; support vector machines; FERET database; LFW database; LZM method; SVM; face recognition; feature extraction method; gender classification; hybrid feature method; local Zernike moments; local binary patterns; support vector machines classifier; Conferences; Databases; Face; Face recognition; Pattern analysis; Signal processing; Support vector machines; FERET; Gender Classification; LFW; Local Binary Patterns; Local Zernike Moments; Support Vector Machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2014 22nd
Conference_Location :
Trabzon
Type :
conf
DOI :
10.1109/SIU.2014.6830519
Filename :
6830519
Link To Document :
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