1. Face Recognition using Artificial Neural Network

2. Contents Problem specification Motivation Design Work done Results
Future work
Demonstration
References

3. Problem Specification
To develop a face recognition system that:
Takes a face image of a person as an input.
Compares the face image of a person with the existing face images that are already stored in the database.
Reports whether it is identified or not.

4. Motivation
Identity fraud is becoming a major concern for all the governments around the globe
Reliable methods of biometric personal identification exists ,but these methods rely on the cooperation of the participants
neural networks are good tool for classification purposes

5. Design Image Sampling Karhunen Loeve (KL) Transform Multilayer
Perceptron
Classification

6. Image sampling

7. KL Transform To reduce the dimensions of the image vector
Based on eigen values and corresponding eigen vectors.

8. Multilayer perceptron

9. Training a neural network
We train our neural network with a large sample of images.
We wish to find the collection of weights that minimizes || TNET - TACTUAL || .

10. Testing
After training is complete then the system as a whole is ready to be used for recognizing any given image.
Testing image is used as an input to our system, the output of the system is compared against the values stored in the database.
System reports whether a match or mismatch.

11. Work Done
Main concern in the project: Face recognition and not face detection.
Database of preprocessed images taken
CMU AMP Face Expression Database
contains 975 images of 13 subjects (75 images of each person)
‘bmp’ format with slightly varying poses, expressions etc
converted into ‘pgm’ format using GIMP
Separate java classes for
K L transform
Multilayer Perceptron (MLP)
Training the MLP

12. Package named JAMA (Java matrix) used
Contains matrix operations like covariance, inverse, transpose etc.
Coding done in java. Reasons being:
To make application platform independent
Java’s ability to handle large numbers
Object oriented: to model real life situations

13. Neural net features: Working
Number of input layer neurons: Number of Eigenvalues
Number of hidden layers: 1
Number of hidden layer neurons: 24(can be changed)
Number of output layer neurons: total number of subjects
Output given by neurons: 0 or 1
Working
Training done with training images
Validation done for the test images
Appropriate message generated if subject is identified or not identified

14. RESULTS Different permutations tried for :
Hidden layer neurons
Output neurons
Form of outputs
Training cycles
Learning rate
Done to bring error in an acceptable range

15. Satisfactory results obtained for following combination :
Input neurons : selected Eigens
Hidden neurons : 24(can be changed)
Output neurons: total number of different subjects
Training cycles:
Learning Rate: 0.3
Error obtained: 2.42E-4
The system identified the subjects presented during training
For subjects not given during training : System refused to identify

16. FUTURE WORK Face detection can be implemented
Processing of image can be incorporated
Output of unidentified persons can be stored for future reference
Ensemble of MLPs can be implemented
Incremental learning can be implemented

17. The mean Image

18. DEMO

19. After training

20. Selecting Image

21. Match Found

22. No Match

23. References
[1] Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, Andrew D. Back, Face Recognition: A Hybrid Neural Network Approach, Technical Report, UMIACS-TR and CS-TR-3608, Institute for Advanced Computer Studies, University of Maryland, 1996.
[2] Wendy S. Yambor, Analysis of PCA-based and Fisher discriminant-based image recognition algorithms, Technical Report CS , Computer Science Department, Colorado State University, July 2000.
[3] Stuart Russel, Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson Education, 2nd Edition.

24. [4] Matthew A. Turk, Alex P. Pentland, Face Recognition Using Eigenfaces, Vision and Modeling Group, The Media Laboratory, Massachusetts Institute of Technology, 1991.
[5] W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition: A Literature Survey, ACM Computing Surveys, 2003, pp
[6] T. De Bie, N. Cristianini, R. Rosipal, Eigenproblems in Pattern Recognition, Handbook of Computational Geometry for Pattern Recognition, Computer Vision, Neurocomputing and Robotics, E. Bayro-Corrochano (editor), Springer-Verlag, Heidelberg, April 2004.
[7] Bai-Bo Zhang, Chang-Shui Zhang, Lower Bounds Estimation to KL Transform in Face Representation and Recognition, Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, 4-5 November 2002.

25. [8] An Introduction to Linear Algebra, : http://www. cs. princeton
[9] John Heaton ,An Introduction to Neural Networks in Java,
[10] H.M. Deitel, P.J. Deitel, Java How to Program, Pearson Education,5th Edition