摘要
结合指纹与指静脉两种生物特征的优点进行多模态特征识别,提出一种特征层动态加权融合匹配算法。在图像预处理的基础上分别提取两模式源的有效特征矢量,根据近邻消除和特殊区域保留原则对特征矢量进行降维;从待识别特征角度对特征点集的相对质量进行评价,根据对双模态特征优和差的分类引入动态加权策略,提高质量较好特征所占权重,削弱低质量及伪特征对识别结果的影响,实现了特征层特征自适应优化融合。在FVC2000公开指纹库和指静脉自建数据库上的测试取得了98.9%的识别率,较指纹、指静脉单模态识别分别提高了6.6%和9.6%,较匹配层加权平均融合识别提高了5.4%。
To study the fusion at feature extraction level for fingerprint and finger vein biometrics, a dynamic weighting matching algorithm based on predictive quality evaluation of interest features is proposed. The proposed approach is based on the fusion of the two traits by extracting independent feature point-sets from the two modalities, and making the two point-sets compatible for concatenation. According to the results of features evaluation, dynamic weighting strategy is introduction for the fusion biometrics. The weight of excellent features in fusion is improved, aiming to weaken the influence of low quality and false features so that better effects of fusion can be achieved. Experimental results based on FVC2000 and self-constructed databases of finger vein show that our scheme achieves 98.9% recognition rate, compared with fingerprint recognition and finger vein recognition increased by 6. 6% and 9. 6% respectively, compared with fusion recognition at matching level increased by 5.4 %.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2012年第9期86-93,共8页
Journal of Chongqing University
基金
中央高校基本科研业务费资助项目(CDJXS11150014)
国家重点实验室访问学者基金(2007DA10512709403)
关键词
自动指纹识别
静脉识别
特征抽取
特征层融合
动态加权
automatic fingerprint verification
vein recognition
feature extraction
feature level fusion
dynamic weighting
