||In this thesis, we present a novel approach to identify people using their palm prints. We use the common scanner to capture the palm print images and don’’t need any fixed pegs to fix the palm. In palm shape recognition machine, users have to put their hand on test plane with some fixed pegs, and this is unsuitable for some users like children. We solve this problem by our approach and make users easier to use the personal identification device.|
We tried the template matching and neural network methods to verify the palm print images. In template matching method, we adopt the linear correlation function to measure the similarity between different palm print images. In this approach, we can achieve about 90% accuracy rate. In neural network approach, we use the standard backpropagation and the scaled conjugate gradient algorithms to train the network. In the backpropagation neural network experiment, we have above 94% accuracy rate. Although the backpropagation neural network has desirable performance the slow convergence is the fatal drawback. In order to produce a significant improvement in the convergence performance of a multilayer perceptron, we have to use high-order information in the training process. So we use the scaled conjugate gradient method on the multilayer perceptron network, and we achieve almost 99.5% accuracy rate.
|| R. Clarke, "Human identification in information systems: Management challenges and public policy issues" Information Technology & People, Vol. 7, No. 4, pp. 6-37, 1994|
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