Impostor Detection Based Finger Veins Applying Machine Learning Methods | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 9, Volume 21, Issue 3, September 2021, Pages 98-111 PDF (1.01 M) | ||
| DOI: https://doi.org/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 | ||
|
Statistics Article View: 237 PDF Download: 159 |
||