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Feature-Fusion Based Biometric Authentication System in Academic Library Access Control

Author

Listed:
  • Lukman Opeyemi Abimbola

    (Department of Computer Science, Faculty of Computing and Informatics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria)

  • Folasade Muibat Ismaila

    (Department of Information Systems, Faculty of Computing and Informatics, Osun State University, Osogbo, Osun State, Nigeria.)

  • Wasiu Oladimeji Ismaila

    (Department of Computer Science, Faculty of Computing and Informatics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria)

  • Ganiyu Ojo Adigun

    (Department of Library Information Systems, Faculty of Arts and Social Sciences, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.)

  • Abigail Bola Adetunji

    (Department of Computer Science, Faculty of Computing and Informatics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria)

  • Muhammed Okikiola Ismaila

    (Department of Nursing, Fountain University, Osogbo, Nigeria)

Abstract

Fingerprint-based authentication systems (FAS) play a crucial role in secure access control, including academic libraries. Conventional fingerprint recognition systems that rely on a single feature extraction technique often struggle to extract robust features, leading to high false positive rates and low accuracy. This research developed a feature-fusion authentication system for academic library access control using multi-feature extraction techniques. 324 university students fingerprint dataset from 81 subjects were captured. The acquired dataset was preprocessed (cropped, contrast adjustment, gray scale, binarization). The Cross Number Algorithm (CNA) and Principal Component Analysis (PCA) were used for feature extraction. The Weighted Sum Rule was used to fuse extracted features from CNA and PCA, generating a unified feature vector. Random Forest Classifier was employed for classification. The results show that CNA–PCA based system achieved accuracy of 96.91%, CNA achieved accuracy of 94.14% and PCA produced accuracy (92.59%).

Suggested Citation

  • Lukman Opeyemi Abimbola & Folasade Muibat Ismaila & Wasiu Oladimeji Ismaila & Ganiyu Ojo Adigun & Abigail Bola Adetunji & Muhammed Okikiola Ismaila, 2026. "Feature-Fusion Based Biometric Authentication System in Academic Library Access Control," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 15(4), pages 791-805, April.
    • Handle: RePEc:bjf:ijltem:v:15:y:2026:i:4:a:2374
    DOI: 10.51583/IJLTEMAS.2026.150400073
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