Description

OBJECTIVE

This system is based on the application of identification of the gender by the features in the face of the human, using DRLBP and the neural networks.

ABSTRACT

In this paper, we describe an experimental investigation to evaluate the significance of different facial regions of a person in the task of gender classification. For this purpose we use NN classifier on face images for gender classification. We perform experiments using different facial regions of varying resolution so that the significance of facial regions in this application can be assessed. According to the results obtained, the upper region of the face proved to be the most significant for the task of gender classification. Moreover, the changes in the resolution of the facial region images do not produce significant changes in the result. Based on the significance of different facial regions, we propose a gender classification method based on fusion of multiple facial regions and show that this method is able to compensate for facial expressions and lead to better overall performance.

EXISTING SYSTEM

• KNN classifier
• Similarity measurements 

PROPOSED SYSTEM

• Neural network
• Pattern identification

BLOCK DIAGRAM

APPLICATIONS

• Card generation applications
• Q-systems

SOFTWARE REQUIREMENTS

• Python
• Opencv
• Numpy

RESULT

In this we have implemented the auto identification of the gender by the use of facial feature extraction method and by the use of neural networks.