@inproceedings{7c856ec438374184991f2dc0e03b1631,
title = "Cinsiyet siniflandirma i{\c c}in birle{\c s}im tabanli {\"o}znitelik vekt{\"o}r{\"u}",
abstract = "In this paper, a feature combining method which can be used in gender classification has been proposed. This method is based on examinating the importance of the pixel regions on face images. In this study, after the analysing commonly used three feature extraction methods (Local binary patterns, discrete cosine transform, histogram of oriented gradients) dimension reduction is achieved via eliminating the redundant face pixels. Then, a new feature vector is obtained by combining the regions considered to be important for each method. When the feature vector's dimension is reducted, it yields the highest success rate with 95.1\% over the 1275 face images.",
keywords = "Automatic gender classification from facial images, Dimension reduction, Face recognition",
author = "Bahri Abaci and Eren Ulucan and Tayfun Akgul",
year = "2014",
doi = "10.1109/SIU.2014.6830453",
language = "T{\"u}rk{\c c}e",
isbn = "9781479948741",
series = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",
publisher = "IEEE Computer Society",
pages = "1211--1214",
booktitle = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",
address = "United States",
note = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 ; Conference date: 23-04-2014 Through 25-04-2014",
}