• 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