Open AccessFingerprint orientation field regularisation via multi‐target regression
Author(s) -
Lin Lu,
Liu Eryun,
Wang Lianghao,
Zhang Ming
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2015.4483
Subject(s) - orientation (vector space) , field (mathematics) , regression , artificial intelligence , computer science , fingerprint (computing) , pattern recognition (psychology) , regression analysis , feature extraction , feature (linguistics) , artificial neural network , algorithm , mathematics , statistics , machine learning , geometry , linguistics , philosophy , pure mathematics
Orientation field estimation is a key step in fingerprint feature extraction and recognition. A complete orientation field estimation algorithm usually consists of two steps, i.e. initial orientation field estimation and post regularisation. In this Letter, a multi‐target regression model to regularise the initial orientation field is proposed. A large number of orientation patches with simulated noises, together with their regression targets are fed to a deep neural networks to train a multi‐target regression model. For a given initial orientation field at testing stage, a refined orientation field is obtained by applying the regression model in patch‐wise and then combining all predicted patches. Experimental results on FVC2002, FVC2004 and FVC2006 databases show remarkable performance compared with state of the art algorithms. Our algorithm is also highly efficient and easy to implement.