By Topic

Advanced Biometric Identification on Face, Gender and Age Recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Ramesha K. ; Dept. of Telecommun. Eng., Vemana Inst. of Technol., Bangalore, India ; Srikanth N. ; K. B. Raja ; Venugopal K. R.
more authors

The face recognition system attains good accuracy in personal identification when they are provided with a large set of training sets. In this paper, we proposed Advanced Biometric Identification on Face, Gender and Age Recognition (ABIFGAR)algorithm for face recognition that yields good results when only small training set is available and it works even with a raining set as small as one image per person. The process is divided into three phases: Pre-processing, Feature Extraction and Classification. The geometric features from a facial image are obtained based on the symmetry of human faces and the variation of gray levels, the positions of eyes, nose and mouth are located by applying the Canny edge operator. The gender and age are classified based on shape and texture information using Posteriori Class Probability and Artificial Neural Network respectively. It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively.

Published in:

Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on

Date of Conference:

27-28 Oct. 2009