Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory

Zhuorong Li; Yunqi Tang

doi:10.1007/s11633-022-1403-7

Volume 20 Issue 6

December 2023

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Zhuorong Li, Yunqi Tang. Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory. Machine Intelligence Research, vol. 20, no. 6, pp.884-896, 2023. https://doi.org/10.1007/s11633-022-1403-7

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Zhuorong Li, Yunqi Tang. Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory. Machine Intelligence Research, vol. 20, no. 6, pp.884-896, 2023. https://doi.org/10.1007/s11633-022-1403-7

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Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory

doi: 10.1007/s11633-022-1403-7

School of Criminal Investigation, People′s Public Security University of China, Beijing 100038, China

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Author Bio:
Zhuorong Li received B. Sc. degree in forensic science from People′s Public Security University of China, China in 2020. She is currently a master student in forensic science at Department of Criminal Investigation, People′s Public Security University of China, China. Her research interests include forensic science and biometric recognition. E-mail: lizhuorong@stu.ppsuc.edu.cn

Yunqi Tang received the B. Sc. degree in computer science from Civil Aviation University of China, China in 2005, the M. Sc. degree in computer science from Beihang University, China in 2008, and the Ph. D. degree in computer science and technology from National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China in 2013. He is currently an associate professor with Department of Criminal Investigation, People′s Public Security University of China, China. His research interests include pattern recognition and machine learning. E-mail: tangyunqi@ppsuc.edu.cn (Corresponding author)
Received Date: 2022-04-22
Accepted Date: 2022-12-12
Publish Online: 2023-04-13
Publish Date: 2023-12-01

Abstract

Abstract

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.
- Image processing,
- convolutional neural network,
- multimodal,
- biometrics,
- fusion

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References(37)

References

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Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory

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