Fingerprint Classification Based on Maximum Variation in Local Orientation Field

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%. Keywords— Fingerprint classification, whorl, right loop, left loop, twin loop, arch, orientation field, singularity points