A comparative study of finger vein recognition by using Learning Vector Quantization

Khusnuliawati, Hardika; Fatichah, Chastine; Soelaiman, Rully

doi:10.12962/j23546026.y2017i2.2311

Cited by 2 publications

(1 citation statement)

References 13 publications

(30 reference statements)

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“…The extracted feature sets from each finger image are fused together into a super-vector based on a serial feature fusion technique. Khusnuliawati et al [ 69 ] compared Scale-Invariant Feature Transform (SIFT) with the LEBP (along with LmBP and LdBP) for feature extraction using the LVQ classifier for the matching process. Dong et al [ 70 ] extracted features using the Difference Symmetric Local Graph Structure (DSLGS) algorithm.…”

Section: Finger Vein Feature Extractionmentioning

confidence: 99%

Feature Extraction for Finger-Vein-Based Identity Recognition

et al. 2021

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This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

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Section: Finger Vein Feature Extractionmentioning

confidence: 99%

Feature Extraction for Finger-Vein-Based Identity Recognition

et al. 2021

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Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion

Jabar Sabat Ahily,

Al Dujaili,

Abdulzahra Al Dulaimi

2024

Open Engineering

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Today, advancements in science and technology have spurred the rapid evolution of systems like electronic banking, demanding precise, swift, and secure identification of individuals based on their distinct traits. Among these traits, fingerprints stand out as a dependable means of identification, finding application in realms such as crime investigation and national border control due to their simplicity and heightened security. The qualities inherent in fingerprint-based identification have led to its widespread adoption over other identification methods. This article proposes a hybrid biometric system that integrates the Gabor filter and scale-invariant feature transform features and then uses support vector machine and K-nearest neighbors as classifiers, aiming to notably enhance authentication systems by mitigating issues seen in single-method biometric systems. Also, principal component analysis is used to reduce dimensions and eliminate redundancy. In this article, the famous database FVC2004 is used. Test results highlight the considerable reliability and accuracy of the proposed combined approach compared to systems reliant on a singular biometric method.

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A comparative study of finger vein recognition by using Learning Vector Quantization

Cited by 2 publications

References 13 publications

Feature Extraction for Finger-Vein-Based Identity Recognition

Feature Extraction for Finger-Vein-Based Identity Recognition

Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion

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