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图学学报

图学学报 ›› 2022, Vol. 43 ›› Issue (2): 279-287.DOI: 10.11996/JG.j.2095-302X.2022020279

• 图像处理与计算机视觉 • 上一篇    下一篇

基于多维匹配距离融合的指节纹识别

  

  1. 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
  • 出版日期:2022-04-30 发布日期:2022-05-07
  • 基金资助:

    国家自然科学基金项目(62076117，61762061)；

    江西省自然科学基金重大项目(20161ACB20004)；

    江西省智慧城市重点实验室项目(20192BCD40002)

Finger-knuckle-print recognition based on multi-dimensional matching distances fusion

  1. 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
  • Online:2022-04-30 Published:2022-05-07
  • Supported by:

    National Natural Science Foundation of China (62076117, 61762061); 

    The Natural Science Foundation of Jiangxi Province, China (20161ACB20004); 

    Jiangxi Key Laboratory of Smart City (20192BCD40002)

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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，提出了一种轻量化且有效的多维匹配距离融合方法。主要思想是基于多种编
码方法中不同匹配距离之间的差异性和互补性，利用支持向量机(SVM)对多种匹配距离所构造出的多维特征向
量进行分类。其具有极强的通用性，易嵌入到现有的基于编码的方法中。在公开的指节纹数据库 PloyU-FKP
上进行了从二维到四维匹配距离的大量实验。结果表明，该方法能够普遍提高认证的性能，EER 最多可降低
22.19%。

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

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.

Key words: finger-knuckle-print recognition, multi-dimensional matching distances, difference complementarity; support vector machine, general method

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