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Score Level Fusion for Iris and Periocular Biometrics Recogniton Based on Deep Learning Cover

Score Level Fusion for Iris and Periocular Biometrics Recogniton Based on Deep Learning

By: Yufei Wang,  Songze Lei,  Yonggang Li,  Bo Liu and  Huan Zuo  
Open Access
|May 2023

Abstract

Traditional iris recognition has high recognition accuracy and low misrecognition rate. However, in the case of mobile terminal or distance, the image resolution and image quality decrease, and the recognition rate also decreases. To solve the above problems, this article is based on deep learning technology, on the basis of single mode state recognition, from different levels of multimodal integration, the iris and the eyes in the score level fusion recognition research, put forward the adaptive dynamic weighted score fusion method, to determine the weighing values can adaptive algorithm of the modal, without artificial specified, dynamic weighting algorithm more flexible, stronger applicability. Experimental results of casIA-Iris-LAMP and CasIA-Iris-Distance Iris database in Chinese Academy of Sciences show that the proposed fusion algorithm has higher recognition accuracy and better recognition performance than the single mode recognition algorithm and the traditional fractional fusion method, which proves the effectiveness of the algorithm.

Language: English
Page range: 21 - 30
Published on: May 26, 2023
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Yufei Wang, Songze Lei, Yonggang Li, Bo Liu, Huan Zuo, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.