Palmprint recognition using wavelet

  • Uma Biradar
  • Smita Jangale
  • Manisha Dale
  • M. A. Joshi
Conference paper


Palmprint is a relatively new biometric feature, and is regarded as one of the most unique, reliable, and stable personal characteristics. A palm is an inner surface of the hand between the wrist and the fingers [1]. Palm has several features to be extracted like principal lines, wrinkles, ridges, singular points, texture and minutiae. Principal lines are the darker line present on the palm. There are 3 principal lines on the palm namely, heart line, head line and life line. Wrinkles are the thinner lines concentrated all over the palm. Normally people do not feel uneasy to have their palmprint images taken for testing. On palms lines and textures are more clearly observable features. Lines are more appealing than texture for human vision. When human beings compare two palmprint images, they instinctively compare line features. But extracting principle lines and creases is not an easy task because it is sometimes difficult to extract the line structures that can discriminate every individual well.


Feature Vector Recognition Rate Wavelet Transform Principal Line Palmprint Image 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    X. Wu, David Zhang, K. Wang, Bo Huang.: Palmprint classification using principal lines, Pattern Recognition 37, 2004, pp.1987-1998MATHCrossRefGoogle Scholar
  2. 2.
    Chang, T. Kuo C.J.: Texture Analysis and Classification with Tree-Structured Wavelet Transform. IEEE Transactions on Image Processing 2(4), 429–441 (1993)CrossRefGoogle Scholar
  3. 3.
    Averbuch, A. Lazar, D. Israeli, M.: Image Compression Using Wavelet Transform and Multiresolution Decomposition. IEEE Trans. Image Processing 5(1), 4–15 (1996)CrossRefGoogle Scholar
  4. 4.
    Zhang, B., Zhang, H. Sam, S.: Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory. IEEE Trans. on Neural Networks 15(1), 166–177 (2004)CrossRefGoogle Scholar
  5. 5.
    X.C. He and N.H.C. Yung. : Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support”, Proceedings of the 17th International Conference on Pattern Recognition, 2:791-794, August 2004CrossRefGoogle Scholar
  6. 6.
    PolyU Palmprint Database.:
  7. 7.
    Daubechies, I.: Ten Lectures on Wavelets. Philadelphia. SIAM, PA (1992)MATHGoogle Scholar
  8. 8.
    Chien, J., Wu, C.C.: Discriminant Wavelet faces and Nearest Feature Classifier for Face Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(12), 1644–1649 (2002)CrossRefGoogle Scholar

Copyright information

© Springer India Pvt. Ltd 2011

Authors and Affiliations

  • Uma Biradar
    • 1
  • Smita Jangale
    • 1
  • Manisha Dale
    • 2
  • M. A. Joshi
    • 3
  1. 1.Vivekanand Institute of TechnologyMumbaiIndia
  2. 2.Modern College of EngineeringMumbaiIndia
  3. 3.Department of Electronics & Telecommunication, COEPMumbaiIndia

Personalised recommendations