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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Monday 23 July 2018

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Face Recognition and Gender Classification Using Features of Lips


Volume 10, Issue 2, October 2014, Pages 337–341

 Face Recognition and Gender Classification Using Features of Lips

Ms. Rutuja G. Shelke and Prof. S. A. Annadate

Original language: English

Received 9 August 2014

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


This paper presents a novel approach for Face Recognition and Gender classification strategy using the features of lips. Here feature extraction is carried out by using Principal component analysis (PCA) and Gabor wavelet. The proposed algorithm converts the RGB image into the YCbCr color space to detect the skin regions in the facial image. But in order to detect facial features the color image is converted in to gray scale image. This method locates the lip region and the mouth region. The gender classification method classifies almost all the images with different image sizes. The best classification rate is achieved by using the methods given in this work. The whole idea is offering a simple, reliable and robust method for extracting features of lips for face recognition and gender identification. For recognition experiments we used face images of persons from different sets of the FERET and AR databases. Recognition experiments with the FERET database (containing photographs of persons) showed that our method can achieve maximal 97-98% first one recognition rate and 0.3-0.4% Equal Error Rate.

Author Keywords: Face recognition, Gender classification, Principal Component Analysis, Gabor wavelet, YCbCr.


How to Cite this Article


Ms. Rutuja G. Shelke and Prof. S. A. Annadate, “Face Recognition and Gender Classification Using Features of Lips,” International Journal of Innovation and Scientific Research, vol. 10, no. 2, pp. 337–341, October 2014.