Palmprint Recognition Using Data Field and PCNN

In this paper, an approach is proposed for palmprint recognition, which uses PCNN and data field theory to extract local statistical structure features of a palmprint. In the method, the data field theory is firstly introduced to obtain a relative palmprint data field, which enhances the palm line information. Then the relative data field is input into a PCNN. Next, the local statistical structure features with four values are extracted from each sub-region. At last, all of local statistic-structural feature vectors are weighted and combined into a long feature vector to represent the palmprint. Experiments show that the novel features can effectively characterize different palmprints.

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