1. Access Control Via Face Recognition Progress Review

2. Group Members  Thilanka Priyankara  Vimalaharan Paskarasundaram  Manosha Silva  Dinusha Perera Supervisors  Shantha Fernando  Dr. Chathura de Silva

3. 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

4. What we found….?  Face recognition techniques Eigen Faces PCA – Principle Component Analysis ICA – Independent Component Analysis EP – Evolutionary Pursuit

5. What we found….? Cont… LDA – Linear Discriminant Analysis EBGM – Elastic Branch Graph Matching

6. Face detection techniques  Knowledge-based methods  Feature invariant approaches  Template matching methods  Appearance-based methods

7. 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

8. Face Recognition Techniques

9. Eigen Faces  Two Step approach Creating Eigen Face Basis Recognition

10.  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…

11.  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…

12.  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…

13. 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

14. EBGM cont…

15. Example

16. 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

17. 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

18. 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)

19. PCA Steps  Compression  Remove the noise Axes of small variance  Matching done with the use of eigen faces

20. Evolutionary Pursuit (EP)  Is an adaptive representation method for image encoding and classification.

21. 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

22. 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

23. Future Works  Select suitable techniques  Prototype Simulate selected algorithms Performance matrix Select the most suitable technique  Design the final product  Development….

24. Thank you