基于多维匹配距离融合的指节纹识别 |
| |
作者姓名: | 黄杰 魏欣 杨子元 闵卫东 |
| |
作者单位: | 1. 南昌大学信息工程学院,江西 南昌 330031;
2. 南昌大学软件学院,江西 南昌 330047;
3. 江西省智慧城市重点实验室,江西 南昌 330047 |
| |
基金项目: | 江西省智慧城市重点实验室项目;国家自然科学基金;江西省自然科学基金重大项目 |
| |
摘 要: | 指节纹识别(FKP)作为一种新型的生物特征识别方式,以其安全性和稳定性而备受关注.基于编码的方法被认为是该领域最有成效法之一,在模板匹配阶段通常根据所提取的特征信息计算出2张图片之间的匹配距离来判断样本.然而,一些模糊样本无法通过单一的匹配距离进行有效区分,从而导致较高的错误接受率和错误拒绝率.针对这一问题,提出了一种...
|
关 键 词: | 指节纹识别 多维匹配距离 差异互补 支持向量机 通用性 |
Finger-knuckle-print recognition based on multi-dimensional matching distances fusion |
| |
Authors: | HUANG Jie WEI Xin YANG Zi-yuan MIN Wei-dong |
| |
Affiliation: | 1. School of Information Engineering, Nanchang University, Nanchang Jiangxi 330031, China;
2. School of Software, Nanchang University, Nanchang Jiangxi 330047, China;
3. Jiangxi Key Laboratory of Smart City, Nanchang Jiangxi 330047, China |
| |
Abstract: | As a novel biometric modality, finger-knuckle-print (FKP) recognition has gained much attention for its
security and stability. Coding-based methods are considered as one of the most effective methods in this field. Such
methods can distinguish samples according to one single matching distance between two images computed from the
extracted features in the template matching stage. However, some fuzzy samples cannot be effectively distinguished
by one single matching distance, leading to false acceptance and false rejection. To address this problem, a
light-weight and effective method based on multi-dimensional matching distances fusion was proposed in this paper. The proposed method utilized the difference and complementarity between different matching distances of multiple
coding-based methods, and applied support vector machine (SVM) to the classification of the multi-dimensional
feature vectors constructed by the multiple matching distances. What’s more, the proposed method is a general
method, which can be easily embedded into the existing coding-based methods. Extensive experiments were
conducted for the range from two-dimensional matching distances to four-dimensional matching distances on the
public FKP database, PolyU-FKP. The results have shown that the proposed method can generally improve their
performances, with a maximum reduction of 22.19% in EER. |
| |
Keywords: | finger-knuckle-print recognition multi-dimensional matching distances difference complementarity support vector machine general method |
本文献已被 万方数据 等数据库收录! |
| 点击此处可从《》浏览原始摘要信息 |
|
点击此处可从《》下载全文 |
|