<![CDATA[The palmprint recognation in this research was being held through several stages, which are image acquisition, preprocessing using histogram equalization,edge detection using sobel operation, feature extraction using Principal Componnent Analysis and face identification using Resilient Propagation. This research use Principal Componnent Analysis as its feature extraction method and Resilient Propagation as its recognition method. This research use 40 training data and 20 testing data wich are gained from PolyU. The final result of the research shows that accuration performance of system using Principal Componnent Analysis and Resilient Propagation by using error tolerance as 1,E-06 and neuron hidden output as 10 are giving best performation that is 65% can be recognized as compared with using the othe error tolerance , neuron output and neuron hidden .]]>
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