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8/3/2019 Face Recognition Using Artificial Neural Network_final
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Face Recognition using
Artificial Neural Network
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Contents
►Problem specification
►Motivation
►Design
►Work done
►Results
►Future work
►Demonstration
►References
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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 theexisting face images that are already stored in thedatabase.
►Reports whether it is identified or not.
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Motivation
►Identity fraud is becoming a major concernfor all the governments around the globe
►Reliable methods of biometric personalidentification exists ,but these methods relyon the cooperation of the participants
►neural networks are good tool forclassification purposes
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Design
Image
Sampling
Karhunen
Loeve (KL)Transform
MultilayerPerceptron
ClassificationImage
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Image sampling
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KL Transform
►To reduce the dimensions of the imagevector
►Based on eigen values and correspondingeigen vectors.
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Multilayer perceptron
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Training a neural network
►We train our neural network with a largesample of images.
►We wish to find the collection of weightsthat minimizes || TNET - T ACTUAL || .
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Testing
► After training is complete then the systemas a whole is ready to be used forrecognizing any given image.
►Testing image is used as an input to oursystem, the output of the system iscompared against the values stored in thedatabase.
►System reports whether a match ormismatch.
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Work Done
►Main concern in the project: Face recognition andnot 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
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►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
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►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 notidentified
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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
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► 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 duringtraining
► For subjects not given during training : System refused toidentify
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FUTURE WORK
►Face detection can be implemented
►Processing of image can be incorporated
►
Output of unidentified persons can bestored for future reference
► Ensemble of MLPs can be implemented
►Incremental learning can be implemented
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The mean Image
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DEMO
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After training
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Selecting Image
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Match Found
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No Match
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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.
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[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 PatternRecognition, 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 theFirst International Conference on Machine Learning and Cybernetics,Beijing, 4-5 November 2002.
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[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