DocumentCode :
3635624
Title :
Effects of the facial and racial features on gender classification
Author :
Özlem Özbudak;Mürvet Kirci;Yüksel Çakir;Ece Olcay Güneş
Author_Institution :
Electronics and Communication Engineering Department, Istanbul Technical University, 34469 Maslak, TURKEY
fYear :
2010
fDate :
4/1/2010 12:00:00 AM
Firstpage :
26
Lastpage :
29
Abstract :
This paper presents an experimental study on examining the effects of facial and racial features on gender classification. In order to show which facial feature is the most influential for gender classification, parts of several face images, such as, forehead, eyebrows, eyes, nose, lip and chin were masked. For dimension reduction, Principal Component Analysis (PCA) and for determination of gender, Fisher Linear Discriminant (FLD) algorithms were applied to masked face images. Moreover, the effects of racial features on gender classification were studied. Experimental results indicated that the nose is the most influential part for gender classification. Furthermore the gender of the Asian people is more easily distinguished than that of the people of African origin.
Keywords :
"Principal component analysis","Pattern recognition","Face recognition","Signal processing algorithms","Facial features","Nose","Fingerprint recognition","Speech recognition","Text recognition","Customer profiles"
Publisher :
ieee
Conference_Titel :
MELECON 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference
ISSN :
2158-8473
Print_ISBN :
978-1-4244-5793-9
Electronic_ISBN :
2158-8481
Type :
conf
DOI :
10.1109/MELCON.2010.5476346
Filename :
5476346
Link To Document :
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