Put our project title as it is

Project Guide Mr.JanaBhaskaraRaoM.E

Asst.Professor

Submitted byM.Srilatha 680552068C.Sai Ram 680552020J.Venkatesh 680552040 K.Ram Nikesh 680552051K.Jagadeesh Babu 680552057

Dept of Electronics and Communication EngineeringAnil Neerulonda Institute of Technology and Sciences

ABSTRACT Face recognition is a biometric identification technology, which

has the most potential. Research on face recognition technology has a great theoretical

and applied value. A new face recognition algorithm for LSI implementation

suitable for embedded applications is implemented. Although many algorithms are there to recognize a face recently

suggested has a relatively stable performance under the variety of environmental disturbances, the problem still lies on computational cost as well as memory usage.

In this project we will propose as algorithm based on a Pseudo fisher face matrix which is derived from generic data sets and down sampled Gabor features, which reduces these costs.

We will compare performance of the proposed method with a traditional one based on Eigen face through FERET data base.

An experimental implementation demonstrated the proposed algorithm draws significant performance rations. The project work is to be implemented using MATLAB.

INTRODUCTION

An automated face authentication system has been an active research subject for several decades.

A face authentication system consists of two face processing tasks. They are

1. Face detection

2. Face recognition Gabor feature extraction is one of the most popular methods

for face recognition. Gabor-Fisher classifier (GFC) method is a combination of

Gabor wavelets and Fisherface.

Gabor Pseudo Fisherface classifier

. . . . 1

Image1

Image2

Image n

preprocessing

Normalized image of Image1

Normalized image of image2

Normalized image of image n

Generic training dataset

Gabor Extract / Down sample

Linear Discriminant Analysis

conclusion This method will improves memory usage as well as

computational cost, it will reduces data size regarding the feature per person