Impostor Detection Based Finger Veins Applying Machine Learning Methods

Ali, Ashraf Tahseen; Abdullah, Hasanen; Fadhil, Mohammed Natiq

doi:10.33103/uot.ijccce.21.3.9

	Impostor Detection Based Finger Veins Applying Machine Learning Methods
IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING
Article 9, Volume 21, Issue 3, 2021, Pages 98-111 PDF (1.01 M)
DOI: 10.33103/uot.ijccce.21.3.9
Authors
Ashraf Tahseen Ali¹; Hasanen Abdullah²; Mohammed Natiq Fadhil³
¹Unversity of Technology
²Computer Sciences Department University of Technology Baghdad Iraq
³Computer Sciences Department, University of Technology, Baghdad, Iraq
Abstract
Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one finger to examine. Impostor recognition based on applying three machine-learning methods will be presented in this article, and then there is a discussion at preprocessing, Linear Discriminant Analysis (LDA) for feature extraction, and k fold cross-validation as an evaluation method. These measures were carried out on two different datasets, which are the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) Dataset and the University of Twente Finger Veins (UTFV) dataset. The classifier with the best results was Support Vector Machine (SVM) and Linear Regression (LR) had the lowest classifier accuracy.
Keywords
Machine learning; Finger Veins; Impostor; Support vector machine; Liner Regression; One Rule

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