Performance Of Resilient Backpropagation Algorithm In Face Recognition
Resilient Backpropagation is a learning heuristics for supervised learning in artificial neural networks. It is simple batch mode training algorithm with fast convergence and minimal storage requirements. The problem in the recognition process is can not be done in few second because its depends...
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| Format: | Monograph |
| Published: |
UTeM
2009
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| Online Access: | http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000054335 http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000054335 http://eprints.utem.edu.my/3088/1/Performance_Of_Resilient_Backpropagation_Algorithm_In_Face_Recognition_-_Suhada_bt_Mohammed_Sapardi_-_TA1650.S93_2009_24_Pages.pdf http://eprints.utem.edu.my/3088/2/Performance_Of_Resilient_Backpropagation_Algorithm_In_Face_Recognition_-_Suhada_bt_Mohammed_Sapardi_-_TA1650.S93_2009.pdf |
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| Summary: | Resilient Backpropagation is a learning heuristics for supervised learning in
artificial neural networks. It is simple batch mode training algorithm with fast
convergence and minimal storage requirements.
The problem in the recognition process is can not be done in few second because
its depends on many factors, including the complexity of the problem, the number of
data points in the training set, the number of weights and biases in the network, the error
goal, and whether the network is being used for pattern recognition (discriminant
analysis) or function approximation (regression).
The aim of this project is to analyse the performance of neural network in which
the algorithm of Resilient Backpropagation has been applied for the purpose of face
recognition. The objectives of the project are to use a built in algorithm using MatLab, to
analyze the performances of the algorithm in the face recognition system and to ensure
that the system is useable and user-friendly (GUI).
The Matlab has been chosen as programming software because it has an image
processing toolbox robot vision and neural network toolbox. The step by step was
followed from creating the programming in M-file, teaching and storing databases to the
system and make a performance testing using different of faces from same 10 people.
The outcome of this recognition process must be 90% and above the system can
recognize the images. |
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