James m Bockarie presentation

Face Recognition using
Artificial Neural Network
newtonedwinbockarie@gmail.com
UNIMTECH BATCH
Presentation on
By
James M Bockarie

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

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.

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

Design
►Now we look at the design
Image
Sampling
Karhunen
Loeve (KL)
Transform
Multilayer
Perceptron
Classification
Image

Image sampling

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

Multilayer perceptron

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 || .

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.

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

►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

► Neural net features:
 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

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

► 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: 100000
 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

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

The mean Image

DEMO

After training

Selecting Image

Match Found

No Match

References
[1] Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, Andrew D. Back, Face
Recognition: A Hybrid Neural Network Approach, Technical Report,
UMIACS-TR-96-16 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-00-103,
Computer Science Department, Colorado State University, July 2000.
[3] Stuart Russel, Peter Norvig, Artificial Intelligence: A Modern
Approach, Pearson Education, 2nd Edition.

[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. 399-458.
[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.

[8] An Introduction to Linear Algebra, :
http://www.cs.princeton.edu/introcs/95linear/
[9] John Heaton ,An Introduction to Neural Networks in Java,
http://www.samspublishing.com
[10] H.M. Deitel, P.J. Deitel, Java How to Program, Pearson
Education,5th Edition

James m Bockarie presentation

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