05-12-2012, 12:25 PM
FACE RECOGNITION USING EIGEN FACES,FISHER FACES AND NEURAL NETWORKS
FACE RECOGNITION.ppt (Size: 2.16 MB / Downloads: 39)
INTRODUCTION
Probably the most common biometric characteristic used by humans
Non-intrusive technique which people generally accept as a biometric characteristic
Overt (user aware) and covert (user unaware) applications.
Subject of intensive research for over 25 years
FACE RECOGNITION METHODS
Direct Correlation
Function-based (Principal Component Analysis, Fisher-based Descriminant method)
Geometry-based methods (elastic graph matching, triangulation, face geoemtry
Principal Component Analysis (PCA)
A face image defines a point in the high-dimensional image space
Different face images share a number of similarities with each other
They can be described by a relatively low-dimensional subspace
They can be projected into an appropriately chosen subspace of eigenfaces and classification can be performed by similarity computation (distance)
Linear Discriminant Analysis (LDA)
Perform dimensionality reduction while preserving as much of the class discriminatory information as possible.
Takes into consideration the scatter within-classes but also the scatter between-classes.
More capable of distinguishing image variation due to identity from other sources such as illumination and expression.
Is LDA always better than PCA?
There has been a tendency in the computer vision community to prefer LDA over PCA.
This is mainly because LDA deals directly with discrimination between classes while PCA does not pay attention to the underlying class structure.
This paper shows that when the training set is small, PCA can outperform LDA.
When the number of samples is large and representative for each class, LDA outperforms PCA.
WORKING OF NEURAL NETWORKS
We use a three layer perceptron neural network.
Consists of an input, hidden and output layers of neurons for classification of input data.
Back propogation algorithms used to update weights according to desired values.
Trained using LDA features.
Input LDA features classify to the class which has the highest similarity