Citation

Tiew, Ken Ming and Ong, Thian Song and Teng, Jackson Horlick and Kua, Alvin Chee Shern and Tian, Hui (2024) Enhancing finger vein recognition systems through local feature aggregation and multiple finger fusion. In: Ninth International Workshop on Pattern Recognition, 2024, 21-23 JUNE 2024, Xiamen, China.

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Abstract

Finger Vein Recognition system is increasingly used for personal recognition. However, unimodal biometrics systems suffer from several limitations, leading to reduced reliability and effectiveness compared to multimodal biometrics systems. This study implements a multi-instance biometric system by leveraging multiple fingers as the input source to enhance the robustness of the finger vein recognition. The works first employs various preprocessing to improve image quality. These techniques include Watershed Segmentation, Morphological Operations, Histogram Equalization, and resizing. Local Binary Pattern (LBP) is then used to extract the features from each finger vein. This study utilizes two fusion methods, including Feature Fusion and Local Feature Aggregation (LFA) to combine results from multiple finger sources. An experimental setup is implemented to evaluate the performance of both fusion methods. The experimental result indicates that the proposed system using LFA achieves higher performance with the lowest Equal Error Rate (EER) of 0.22% and 0.25% for the UTFVP dataset and SDUMLA-HMT dataset, respectively. This emphasizes the ability of the LFA to enhance the robustness of the finger vein recognition system, contributing to future research.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Finger Vein Recognition
Subjects:	Q Science > QL Zoology T Technology > T Technology (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Dec 2024 03:19
Last Modified:	03 Dec 2024 03:19
URII:	http://shdl.mmu.edu.my/id/eprint/13181

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