Abstract
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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Wang, Y., Zhao, J., Sun, G. (2013). Palmprint Recognition Using Data Field and PCNN. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2013. Lecture Notes in Computer Science, vol 8251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_21
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DOI: https://doi.org/10.1007/978-3-642-45062-4_21
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