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_DUT, respectively.