Automatic fingerprint classification based on embedded Hidden Markov Models

Automatic fingerprint classification provides an important indexing scheme to facilitate efficient matching in large-scale fingerprint databases for any Automatic Fingerprint Identification System (AFIS). A novel method of fingerprint classification, which is based on embedded Hidden Markov Models (HMM) and the fingerprint's orientation field, is described in this paper. The accurate and robust fingerprint classification can be achieved with extracting features from a fingerprint, forming the samples of observation vectors, and training the embedded HMM. Results are presented on two fingerprint databases, Fingdb and Finger/spl I.bar/DUT, respectively.