Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Palmprint identification using feature-level fusion
Pattern Recognition
Palmprint verification based on principal lines
Pattern Recognition
Palmprint verification based on robust line orientation code
Pattern Recognition
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
An automated palmprint recognition system
Image and Vision Computing
Palmprint verification using hierarchical decomposition
Pattern Recognition
Characterization of palmprints by wavelet signatures via directional context modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A face and palmprint recognition approach based on discriminant DCT feature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Palm line extraction and matching for personal authentication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.