Automatic fingerprint classification, based on embedded hidden Markov models

被引：1
|
作者：
Guo, H [1 ]
Ou, ZY [1 ]
He, Y [1 ]
机构：
[1] Dalian Univ Technol, Sch Mech Engn, CAD&CG Lab, Dalian 116024, Peoples R China
来源：
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年
关键词：
fingerprint identification; fingerprint classification; hidden Markov models; orientation field;
D O I：
10.1109/ICMLC.2003.1260098
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：3033 / 3038
页数：6
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