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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