Access Control Via Face Recognition

Progress Review

Group Members

Thilanka Priyankara Vimalaharan Paskarasundaram Manosha Silva Dinusha Perera

Supervisors Shantha Fernando Dr. Chathura de Silva

What we are doing….?

As this area is a highly research area we have to go through lots of research papers

No exact solution has been found Search for techniques Search for algorithms that support those

techniques

What we found….?

Face recognition techniques Eigen Faces PCA – Principle Component Analysis ICA – Independent Component

Analysis EP – Evolutionary Pursuit

What we found….? Cont…

LDA – Linear Discriminant Analysis EBGM – Elastic Branch Graph

Matching

Face detection techniques

Knowledge-based methods Feature invariant approaches Template matching methods Appearance-based methods

Face detection techniques cont…

There is an open source libraries to do face detection in a given image. Ex : OpenCV

Code reuse will reduce time taken for development

We are going to check the usage of OpenCV for our project for face detection

Face Recognition Techniques

Eigen Faces

Two Step approach

Creating Eigen Face Basis

Recognition

Eigen Face Basis Collect images of faces (same dimension)

Put into vectors Get sum of all vectors Get the average Get the difference and save

Eigen Faces cont…

Face Recognition Get the new image of the person being

identified (with previous dimension) Put into a vector Get the difference Use predefined threshold

Eigen Faces cont…

Pros More images of one person increase the

accuracy sharply Better than Feature matching

Cons When adding new image eigen face basis

should be regenated

Eigen Faces cont…

EBGM Define a face as a graph Nodes

Fiducial points Pupils Corners of the mouth Tip of the nose

Represented with a bunch of features from the same fiducial point (e.g., male/female, eyes opened/shut, etc)

Edges Labeled with the distance between fiducial points

EBGM cont…

Example

Problems in this approach

Face by itself is too variable Beards not represented properly Glasses too variable

Illumination still plays a big role Test done with low-resolution images Face “detection” very slow using this

approach

Principal Component Analysis (PCA)

PCA is a data-reduction method that finds an alternative set of parameters for a set of raw data

A face image defines a point in the high-dimensional image space

Different face images share a number of similarities with each other

Principal Component Analysis (PCA)

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)

PCA Steps

Compression Remove the noise

Axes of small variance Matching done with the use of eigen

faces

Evolutionary Pursuit (EP)

Is an adaptive representation method for image encoding and classification.

Evolutionary Pursuit cont…

Dimensionality reduction using PCA method Apply the whitening transformation on the

reduced matrix Begin the evolution loop

Apply rotation transformation according to the values in the GA

Compute the fitness value Change the rotation angles and do the first

operation, continue the evolution loop until fitness value is maximized

Evolutionary Pursuit cont…

This method uses Genetic algorithms to determine the best fit

Improved face recognition performance when compared with Eigen faces

Displays better generalization ability than the Fisherfaces

Future Works

Select suitable techniques Prototype

Simulate selected algorithms Performance matrix Select the most suitable technique

Design the final product Development….

Thank you