Finger-Vein Classification Using Granular Support Vector Machine

  • Ali SelamatEmail author
  • Roliana Ibrahim
  • Sani Suleiman Isah
  • Ondrej Krejcar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)


The protection of control and intelligent systems across networks and interconnected components is a significant concern. Biometric systems are smart systems that ensure the safety and protection of the information stored across these systems. A breach of security in a biometric system is a breach in the overall security of data and privacy. Therefore, the advancement in improving the safety of biometric systems forms part of ensuring a robust security system. In this paper, we aimed at strengthening the finger vein classification that is acknowledged to be a fraud-proof unimodal biometric trait. Despite several attempts to enhance finger-vein recognition by researchers, the classification accuracy and performance is still a significant concern in this research. This is due to high dimensionality and invariability associated with finger-vein image features as well as the inability of small training samples to give high accuracy for the finger-vein classifications. We aim to fill this gap by representing the finger vein features in the form of information granules using an interval-based hyperbox granular approach and then apply a dimensionality reduction on these features using principal component analysis (PCA). We further apply a granular classification using an improved granular support vector machine (GSVM) technique based on weighted linear loss function to avoid overfitting and yield better generalization performance and enhance classification accuracy. We named our approach PCA-GSVM. The experimental results show that the classification of finger-vein granular features provides better results when compared with some state-of-the-art biometric techniques used in multimodal biometric systems.


Granular computing Cybersecurity Biometric Finger-vein Support vector machines 



This research has been funded by Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876 and the Fundamental Research Grant Scheme (FRGS) Vot 5F073 supported under Ministry of Education Malaysia. The work is partially supported by the SPEV project, University of Hradec Kralove, FIM, Czech Republic (ID: 21xx-2020). We are also grateful for the support of Ph.D. student Sebastien Mambou in consultations regarding application aspects.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ali Selamat
    • 1
    Email author
  • Roliana Ibrahim
    • 2
  • Sani Suleiman Isah
    • 2
  • Ondrej Krejcar
    • 3
  1. 1.Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi MalaysiaKuala LumpurMalaysia
  2. 2.School of Computing, Faculty of Engineering, UTM and Media and Games Center of Excellence (MagicX), Universiti Teknologi MalaysiaJohor BahruMalaysia
  3. 3.Faculty of Informatics and Management,University of Hradec KraloveHradec KraloveCzech Republic

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