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• [P1184] Fingerprint Classification Based on Maximum Variation in Local Orientation Field
  Author(s): Milind E. Rane, Pradeep M. Patil
  
  

  Abstract
  Fingerprint classification provides an important indexing mechanism in a fingerprint database. Accurate and consistent classification can greatly reduce fingerprint-matching time and computational complexity for a large database as the input fingerprint needs to be matched only with a subset of the fingerprint database. Classification into six major categories (whorl, right loop, left loop, twin loop, arch, and tented arch) with no reject options yields an accuracy of 89.7 %. The overall accuracy is improved to 91.5 % if the arch and tented arch are merged as a single class. The penetration rate of the proposed classification system is 88.9%.

  
   

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