基于支持向量机的指纹图像质量分类方法  

Quality Classification Method for Fingerprint Image Based on Support Vector Machine

作　　者：张宇[1] 尹义龙[1] 骆功庆[1] 

机构地区：[1]山东大学计算机科学与技术学院,济南250101

出　　处：《模式识别与人工智能》2009年第1期129-135,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60403010);山东省优秀中青年科学家科研奖励基金项目(No.2006BS01008);山东省科技攻关项目(No.2005GG3201089);山东省高新技术自主创新工程专项项目(No.2007ZCB01030)资助

摘　　要：指纹图像的质量测量与评价,在指纹图像分割、增强及指纹匹配等环节都有重要应用.同时,指纹图像的质量分类,对指纹识别算法的适用性研究也有重要意义.本文提出一种基于支持向量机的指纹图像质量分类方法.该方法选择梯度、Gabor特征、方向对比度等指标,利用支持向量机有效实现指纹图像质量分类.并采用少类样本合成过采样技术(SMOTE)降低指纹图像质量好坏的类别不平衡问题对分类的影响.理论分析和实验结果都表明该方法能够较为有效地提高指纹图像质量分类的正确率.In an automatic fingerprint identification system, estimating the quality of fingerprint image has significant value for segmentation, enhancement and matching processes. Besides, the quality classification of fingerprint image is of paramount significance in the applicability research of fingerprint recognition algorithm. In this paper, a method for quality classification of fingerprint image is proposed based on the support vector machine （SVM）. The gradient, Gabor feature, and directional contrast are used as the quality index, and SVM is applied to achieve quality classification of fingerprint image. Meanwhile, synthetic minority over sampling technique （SMOTE） method is employed to reduce the influence of class imbalance problem. Both the theoretical analysis and the experimental results indicate the validity of the proposed method.

关 键 词：指纹 图像质量 质量分类 支持向量机 少类样本合成过采样技术(SMOTE) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]