The gender recognition system with large sets of training sets for personal identification normally attains good accuracy. The features set are applied to three different applications: Pre-processing, Feature Extraction and Classification. The gender are classified on the basis of distance between eyebrow to eye, eyebrow to nose top, nose top to mouth, eye to mouth, left eye to right eye, width of nose, width of mouth. First to extract these features by using Viola Jones algorithm and then apply Artificial Neural Network. The features set is applied to three different applications: face recognition, facial expressions recognition and gender classification. In this paper described two phases such as feature extraction phase and classification phase. The proposed system produced very promising recognition rates for our applications with same set of features and classifiers.

 

Published In : IJCSN Journal Volume 2, Issue 6

Date of Publication : 01 December 2013

Pages : 108 - 113

Figures : 04

Tables : 01

Publication Link : ijcsn.org/IJCSN-2013/2-6/IJCSN-2013-2-6-144.pdf

 

 

 

Ms. Anushri Jaswante : Computer Science & Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India.

Dr. Asif Ullah Khan : Computer Science & Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India.

Dr. Bhupesh Gour : Computer Science & Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India.

 

 

 

 

 

 

 

Feature Extraction

Gender Classification

Back Propagation neural network.

 

 

 

 

A fast and efficient gender classification system based on facial features has been developed to classify the images on the bases of gender. The proposed methodology give 100% accurate results in identifying male and female images. This paper presents the results with hundred male and hundred female images. The proposed system has a low complexity and is suitable for real time implementations.

 

 

 

 

 

 

 

 

 

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