Hybrid Approach for Palmprint Recognition Using Compound Features

  • N. L. Manasa
  • A. Govardhan
  • Ch. Satyanarayana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)


As patterns in a palmprint have abundance of invariance, the inter-class and intra-class variability of these features makes it difficult for just one set of features to capture this variability. This inspires us to propose a hybrid feature extraction and fusion approach for palmprint recognition based on texture information available in the palm. Scale, shift and rotation (Affine) invariance, good directional sensitivity properties of Dual-tree Complex Wavelets makes it a choice to capture texture features at global level. Local Binary Pattern on the other hand being gray-scale and rotation invariant, captures local fine textures effectively. These local features are sensitive to position and orientation of the palm image. Canonical Correlation Analysis is used to combine the features at the descriptor level which ensures that the information captured from both the features are maximally correlated and eliminate the redundant information giving a more compact representation. Experimental results demonstrate an accuracy of 97.2% at an EER of 3.2% on CASIA palmprint database.


Discrete Wavelet Transform Local Binary Pattern Canonical Correlation Analysis Equal Error Rate Complex Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • N. L. Manasa
    • 1
  • A. Govardhan
    • 1
  • Ch. Satyanarayana
    • 1
  1. 1.Jawaharlal Nehru Technological UniversityIndia

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