Title :
Automatic fingerprint classification based on embedded Hidden Markov Models
Author :
Guo, Hao ; Ou, Zong-Ying ; He, Yang
Author_Institution :
Sch. of Mech. Eng., Dalian Univ. of Technol., China
Abstract :
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.
Keywords :
feature extraction; fingerprint identification; hidden Markov models; pattern classification; visual databases; AFIS; Hidden Markov Models; automatic fingerprint classification; automatic fingerprint identification system; embedded HMM; feature extraction; fingerprint databases; indexing scheme; observation vectors; Artificial neural networks; Error analysis; Fingerprint recognition; Hidden Markov models; Image matching; Large-scale systems; Principal component analysis; Probability; Robustness; Spatial databases;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1260098
