[1]PAN Zaiyu,XU Jiameng,WANG Jun,et al.Palmprint and palm vein recognition method based on modal information evaluation strategy[J].CAAI Transactions on Intelligent Systems,2024,19(5):1136-1148.[doi:10.11992/tis.202310002]
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Palmprint and palm vein recognition method based on modal information evaluation strategy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1136-1148 Column: 学术论文—机器感知与模式识别 Public date: 2024-09-05
Title:
Palmprint and palm vein recognition method based on modal information evaluation strategy
Author(s):
PAN Zaiyu1;  XU Jiameng1;  WANG Jun1;  JIA Wei2
1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;
2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
Keywords:
biometric recognition; palmprint image; palmvein image; multimodal biometric databases; modal information evaluation strategy; category confidence level; multimodal fusion; palmprint and palmvein fusion recognition
CLC:
TP30
DOI:
10.11992/tis.202310002
Abstract:
Multimodal biometric recognition has gained widespread attention in the industry due to its excellent performance and robust reliability. However, traditional multimodal biometric recognition methods usually fuse directly at the feature or matching layer and rarely consider the differences in fusion effects caused by the quality of modal samples. Moreover, research on multimodal biometric recognition methods is restricted by the absence of large-scale publicly available multimodal biometric databases. Therefore, first, a hand multimodal data acquisition device is designed, and a hand multimodal database is created for the validation and evaluation of multimodal biometric recognition methods. Second, a palmprint and palm vein fusion recognition method is proposed based on a modal information evaluation strategy. It uses the category confidence level corresponding to ground truth sample labels to assess the information level of each modal feature. Thus, the model adaptively assigns weights according to the contribution rates of different modes during the identity recognition fusion process. This method outperforms other recognition methods by achieving the highest recognition rate on two public databases and one self-built multimodal biometric database.
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