摘要
针对不同传感器采集的指纹难以进行统一准确的分割的问题,提出主频率带能量比值,归一化的灰度均值及灰度对比度值三个更为通用的指纹描述特征,并利用这三个指纹特征,使用SVM方法训练一个线性分类器,对指纹进行有效分割.实验表明,该方法不仅实现了对指纹的准确分割,在FVC2002指纹数据库上平均分割误差约为2%,并且该分割方法具有很好的通用性,适用各种传感器采集的指纹.
Fingerprints are always captured from different sensors,and to segment these fingerprints accurately with a united algorithm is very difficult.A universal algorithm based on linear classifier is proposed to sovle the problem.This algorithm uses three new general features extracted from the fingerprint to train a linear classifier with SVM,and then segment the fingerprints with the classifier.These new general features include the dominant frequency band energy ratio,the normalized gray mean value and the gray contrast.The experiments in the fingerprint database FVC2002 show that,this algorithm can segment the fingerprints accurately,with the average error rate of only about 2%,and it is universal in the fingerprint database acquired from different sensors.
出处
《微电子学与计算机》
CSCD
北大核心
2011年第12期124-128,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(600972146,60602025)
中国博士后科学基金(20110491661)
中央高校基本科研业务最专项资金
关键词
指纹分割
线性分类器
主频率
fingerprint segmentation
linear classifier
dominant frequency
