Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition

Citation

Teng, Jackson Horlick and Ong, Thian Song and Tee, Connie and Sonai Muthu Anbananthen, Kalaiarasi and Min, Pa Pa (2022) Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition. Algorithms, 15 (5). p. 161. ISSN 1999-4893

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Abstract

The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively.

Item Type: Article
Uncontrolled Keywords: multi-instance finger vein biometrics, histogram of oriented gradients, score level fusion, Bayesian hyperparameter optimization
Subjects: Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 01 Jul 2022 02:08
Last Modified: 01 Jul 2022 02:08
URII: http://shdl.mmu.edu.my/id/eprint/10113

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