Performance Of Resilient Backpropagation Algorithm In Face Recognition

Suhada, Mohammed Sapardi (2009) Performance Of Resilient Backpropagation Algorithm In Face Recognition. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

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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