DSP Charcterization of palmprints  


Characterization of Palmprints by Wavelet Signatures via Directional Context Modeling


The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual.



Project is palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain and the directional context of each wavelet sub band defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results.It also provides a convenient classification strategy and more accurate identification.

Applications:
  • Most commercial companies that provide fingerprint capabilities have added capabilities for storing and ching palm print records
  • Several state and local agencies have implemented palm systems
  • Federal Bureau of Investigation (FBI), Criminal Justice Information Services (CJIS) Division houses the largest collection of criminal history information in the world.


 
 
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