Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory

doi:10.1007/s11633-022-1403-7

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory
	Zhuorong Li; Yunqi Tang
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2023
卷号	20 期号:6 页码:884-896
摘要	Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques' vulnerability to attack and poor performance in low-quality data. However, difficulties still exist in how to unify the meaning of heterogeneous scores more effectively. Aside from the matching scores themselves, the importance of the ranking information they include has been undervalued in previous studies. This study concentrates on matching scores and their ranking information and suggests the ranking partition collision (RPC) theory from the standpoint of the worth of scores. To meet both forensic and judicial needs, this paper proposes a method that employs a neural network to fuse biometrics at the score level. In addition, this paper constructs a virtual homologous dataset and conducts experiments on it. Experimental results demonstrate that the proposed method achieves an accuracy of 100% in both mAP and Rank1. To show the efficiency of the proposed method in practical applications, this work carries out more experiments utilizing real-world data. The results show that the proposed approach maintains a Rank1 accuracy of 99.2% on the million-scale database. It offers a novel approach to fusion at the score level.
关键词	Image processing, convolutional neural network, multimodal, biometrics, fusion
DOI	10.1007/s11633-022-1403-7
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/56016
专题	学术期刊_Machine Intelligence Research
作者单位	School of Criminal Investigation, People's Public Security University of China, Beijing 100038, China
推荐引用方式 GB/T 7714	Zhuorong Li,Yunqi Tang. Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory[J]. Machine Intelligence Research,2023,20(6):884-896.
APA	Zhuorong Li,&Yunqi Tang.(2023).Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory.Machine Intelligence Research,20(6),884-896.
MLA	Zhuorong Li,et al."Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory".Machine Intelligence Research 20.6(2023):884-896.

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