Abstract
Feature extraction is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Neighborhood rough set (NRS) based feature extracting algorithm is able to delete most of the redundant and irrelevant features, which avoid the step of data discretization and hence decreased the information lost in preprocess. In this paper, we firstly introduce the basic definitions and operations of NRS, and propose a palmprint recognition method based on NRS. The neighborhood model is used to reduce the attributes and extract the recognition features. Experimental results on PolyU palmprint database demonstrate that the proposed method is effective and feasible for palmprint recognition.
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Zhang, S., Liu, J. (2010). Palmprint Recognition Based on Neighborhood Rough Set. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_81
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DOI: https://doi.org/10.1007/978-3-642-14922-1_81
Publisher Name: Springer, Berlin, Heidelberg
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