FACE RECOGNTION USINGEIGEN VALUES AND NEURAL NETWORKS

INTRODUCTION

Traditional face recognition algorithms works on a single input image for feature extraction

Low accuracy Our implementation works on multiple images

for feature extraction

For the system that we have developed we initially extract the features of the face using Principal Component Analysis and then feed the extracted Eigen values into the input of a feed forward back-propagation neural network.

We have taken a data set of 8 persons with 10 images of each person. The neural network consists of 1 input layer(10 neurons), 3 hidden

layers(10 neurons each) and 1 output layer(1 neuron).

EIGEN FACE

In Eigen Faces approach, the face images are decomposed into a small set of characteristic feature images called “Eigen-faces” (which contain the common features in a face) which are extracted from the original training set of images by means of principal component analysis. An initial set of images is acquires(training set) and the Eigen Faces from the training set are calculated and only M images that correspond to the highest Eigen Values define the face space.

NEURAL NETWORKS

A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. A neural network is initially "trained" or fed large amounts of data and rules about data relationships. A program can then tell the network how to behave in response to an external or can initiate activity on its own. Neural networks are typically organized in layers. Layers are made up of a number of interconnected 'nodes' which contain an 'activation function'. Patterns are presented to the network via the 'input layer', which communicates to one or more 'hidden layers' where the actual processing is done via a system of weighted 'connections'. The hidden layers then link to an 'output layer' where the answer is output.

CONTROL FLOW CHART

OPEN WEB CAMERA ADD PERSON

  

SAVE EIGEN VALUES IN A FILE

INPUT EIGEN VALUES TO NEURAL NETWORK

PASS INPUT VALUES THROUGH THE NETWORK

ERROR CALCULATION USING DESIRED OUTPUT

BACK PROPAGATING CHANGING WEIGHTS

FOR EPOCHS LESS THAN ASSIGNED

VALUE

MATCH FACE WITH MOST NEAR VALUE OR

LEAST ERROR

IMPLEMENTATION