计算机技术与发展2017，Vol.27Issue(1)：1-5,5.DOI:10.3969/j.issn.1673-629X.2017.01.001
基于回归的指纹方向场估计
Fingerprint Orientation Field Estimation Based on Regression
摘要
Abstract
Fingerprint orientation filed is crucial for fingerprint singularity detection, feature extraction and matching, classification and recognition,etc. . Many methods have been proposed for estimating fingerprint orientation field,mostly in two steps:estimation and regu-larization ( or de-noising) . Yet,motivated by emerging deep learning techniques,a regression-based end-to-end fingerprint orientation field estimation method is proposed. It directly estimates the ridge orientation at the center of a fingerprint image patch through a regres-sion function from the texture feature of the image patch. Given a fingerprint image,the total variation model is applied to decompose it into cartoon and texture components. Then the texture component is divided into patches,using a Deep Convolutional Neural Network ( DCNN) to estimate the ridge orientation at the center of each patch. The fingerprint images in NIST SD14 are adopted as training data to learn the DCNN-based regression function,and evaluate the proposed method on the FVC2002 and FVC2004 databases. The experi-mental results indicate that compared with the existing algorithms,the algorithm is simple and easy to operate,and has better anti-noise a-bility,which can accurately estimate the orientation field of singular point and its surroundings,and effectively raise the fingerprint recog-nition rate.关键词
指纹方向场/卷积神经网络/回归/总变差模型Key words
fingerprint orientation field/convolutional neural network/regression/total variation model分类
信息技术与安全科学引用本文复制引用
戴晓薇,赵启军..基于回归的指纹方向场估计[J].计算机技术与发展,2017,27(1):1-5,5.基金项目
国家自然科学基金资助项目(61202161) （61202161）
