Fingerprint classification techniques

TL;DRAbstract

Today, most biometrics research groups are tackling the challenging problem of an automatic fingerprint identification system (AFIS) using large databases. Since AFIS dedicates most of its processing time to searching for the best-matched fingerprint, searching over the entire fingerprint database is rather inefficient. It is proposed that the database be divided into sub-databases, each containing only fingerprints of the same pattern or class. Fingerprint classification is then an important first step in directing the search only to the appropriate sub-database, thus reducing the extent of searching of the large database. The main objective of this thesis is to propose a classification technique to reliably classify a fingerprint into one of six well-known classes: plain arch, tented arch, right loop, left loop, whorl and twin loop. The fingerprint classification technique proposed in this thesis has achieved good results owing to the improvement in a number of processing steps the a

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