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

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%).

Fingerprint-based authentication systems, Academic libraries, Cross Number Algorithm, Principal Component Analysis, Weighted Sum Rule

1. Afolabi A. O. and Adagunodo R. (2012). “Implementation of an Improved Facial Recognition Algorithm in a Web-based Learning System,” International Journal of Engineering and Technology, 2(11), 1885–1892. [Google Scholar] [Crossref]

2. Afolabi A.O., Falohun A.S., and Adedeji O.T. (2019). “Securing E-Library System with Bimodal Biometric Technique,” Annal Biostatistics & Biometric Applications, 3(5), 1–7. [Google Scholar] [Crossref]

3. Ahmed, S., Rahman, T., and Khan, M. (2022). “Multi-modal biometric authentication for library access control,” International Journal of Biometrics, 11(4), 112–125. [Google Scholar] [Crossref]

4. Ahmad, T., Patel, R., and Khan, M. (2021). “Decision-level fusion in biometric systems: A review,” Biometric Technology Today, 7(3), 89–103. [Google Scholar] [Crossref]

5. Al-juboori A. M., Bui W., Wu X., and Zhao Q. (2013). “Palm Vein Verification Using Gabor Filter,” IJCSI International Journal of Computer Science Issues, 10(1), 678–684. [Google Scholar] [Crossref]

6. Atul M. Gonsai and Nilesh N. Soni (2007). “Biometric Authenticated Library Network Model for Information Sharing,” International Caliber-2007, Panjab University, Chandigarh, pp. 491–499. [Google Scholar] [Crossref]

7. Ayeswarya S. and John Singh K. (2024). “FacekeyID: An adaptive weighted sum score based fusion framework for continuous user authentication,” Engineering Research Express. [Google Scholar] [Crossref]

8. Bhatia, R., and Purohit, S. (2020). “Biometric systems: Physiological and behavioral traits,” International Journal of Security Studies, 9(1), 23–37. [Google Scholar] [Crossref]

9. Chen, X., Wang, Y., and Zhang, L. (2023). “Fingerprint quality variations and their impact on authentication systems,” Biometric Technology Today, 8(4), 112–125. [Google Scholar] [Crossref]

10. Deepesh Raj (2011). “A Realtime Face Recognition System using PCA and various Distance Classifiers,” Computer Vision and Processing, 5(3), 15–22. [Google Scholar] [Crossref]

11. Fangfang Dong (2024). “Random Forest Algorithm for HR Data Classification and Performance Analysis in Cloud Environments,” (IJACSA) International Journal of Advanced Computer Science and Applications, 15(11), 501–510. [Google Scholar] [Crossref]

12. Ismaila W. O., Ismaila Folasade M., and Falohun Adeleye S. (2017). “Soft Computing Approach to Multi-Modal Biometric System,” American International Journal of Research in Science, Technology, Engineering & Mathematics, 20(1), 66–72. [Google Scholar] [Crossref]

13. Johnson, R., and Lee, S. (2022). “Fingerprint recognition: A cost-effective solution for secure access control,” Journal of Biometric Engineering, 7(1), 23–37. [Google Scholar] [Crossref]

14. Jain, A., Ross, A., and Prabhakar, S. (2020). “Principal Component Analysis in biometric systems,” Journal of Computational Biometrics, 6(3), 112–128. [Google Scholar] [Crossref]

15. Jose-Luis S-R., Jimenez-Cruz R., Villuendas-Rey Y., and Yanez-Marquez (2023). “Random Forest algorithm for the classification of spectral data of astronomical objects,” Algorithms, 16(6), 293. [Google Scholar] [Crossref]

16. Kisku, D., Nandakumar, K., and Jain, A. (2021). “Template storage and encryption in biometric systems,” Journal of Biometric Engineering, 7(4), 89–103. [Google Scholar] [Crossref]

17. Kumar, P., and Singh, S. (2023). “Weighted sum rule fusion for improved biometric recognition,” Biometric Systems Review, 11(3), 45–60. [Google Scholar] [Crossref]

18. Kumar, A., and Singh, R. (2021). “Hybrid feature extraction for fingerprint and palmprint recognition,” International Journal of Biometrics, 10(3), 67–82. [Google Scholar] [Crossref]

19. Linlin Shen, Li Bai, and Zzhen J.i. (2011). “FPCODE: An efficient approach for multi-modal biometrics,” International Journal of Pattern Recognition and Artificial Intelligence, 25(2), 273–286. [Google Scholar] [Crossref]

20. Li, H., Zhang, W., and Wang, Y. (2021). “Dimensionality reduction in fingerprint feature extraction using PCA,” Journal of Computational Biometrics, 5(2), 112–128. [Google Scholar] [Crossref]

21. Malathi, R., and Sudha, V. (2019). “Sensor-level fusion in biometric systems,” Journal of Biometric Research, 5(1), 34–50. [Google Scholar] [Crossref]

22. Margaret Rouse (2019). “Biometrics.” Available at: https://searchsecurity.techtarget.com/definition/biometrics [Google Scholar] [Crossref]

23. Mote P. and Zope P.H. (2012). “Multimodal Biometric system using Gabor Filter,” International Journal of Advanced Trends in Computer Science and Engineering, 1(2). [Google Scholar] [Crossref]

24. Ramesh M. R. (2012). “Biometric Recognition: A New Approach for Library Patron Authentication,” International Journal of Library Science, 1(5), 72–74. [Google Scholar] [Crossref]

25. Patil, S., and Shinde, G. (2022). “Biometric applications in academic libraries,” Library Hi Tech, 40(2), 345–360. [Google Scholar] [Crossref]

26. Patil, S., and Shinde, V. (2022). “Cross Number Algorithm for fingerprint feature extraction,” Journal of Biometric Engineering, 8(3), 112–125. [Google Scholar] [Crossref]

27. Pandey, S., Thakur, R., and Kumar, A. (2021). “Feature-level fusion using Principal Component Analysis,” Journal of Computational Biometrics, 7(1), 23–37. [Google Scholar] [Crossref]

28. Pandija S., Ramanathan N., Subashinee S., Vignesh J., and Mythili E. (2020). “Library Management System Using Fingerprint Recognition,” International Journal of Innovative Research in Technology, 6(12), 566–570. [Google Scholar] [Crossref]

29. Phillips P. J., Flynn P. J., Scruggs T., Bowyer K. W., Chang J., Hoffman K., Marques J., Min J., and Worek W. (2005). “Overview of the Face Recognition Grand Challenge,” in Computer Vision and Pattern Recognition (CVPR 2005), IEEE Computer Society Conference on, pp. 947–954. [Google Scholar] [Crossref]

30. Patel, R., Khan, M., and Alam, A. (2022). “Continuous authentication using behavioral biometrics,” Biometric Technology Today, 9(1), 67–82. [Google Scholar] [Crossref]

31. Pratibha S. Yalagi and Prachi V. Mane (2021). “Smart library automation using face recognition,” Journal of Physics: Conference Series, 1854, 012041. [Google Scholar] [Crossref]

32. Probst, P., Wright, M.N., and Boulesteix, A.L. (2019). “Hyperparameters and tuning strategies for random forest,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9, 1301. [Google Scholar] [Crossref]

33. Shweta Shirpurkar and R.A. Ingolikar (2018). “Data Mining of Facial Features using Random Forest Algorithm for Person Classification,” IJCRT, 6(1), 2112–2120. [Google Scholar] [Crossref]

34. Smith, J., Johnson, R., and Lee, S. (2021). “Security vulnerabilities in traditional authentication methods,” Journal of Cybersecurity, 6(3), 112–125. [Google Scholar] [Crossref]

35. Thakur, R., Pandey, S., and Kumar, A. (2021). “Score-level fusion techniques in biometric systems,” Journal of Computational Biometrics, 6(2), 67–82. [Google Scholar] [Crossref]

36. Turk, M., and Pentland, A. (1991). “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, 3(1), 71–86. [Google Scholar] [Crossref]

37. Traore O. E., Kopoin N. C., N’takpe T. G., and Oumtanaga S. (2025). “Optimization of Feature Extraction for the Prediction of Macromolecular Interactions: OTE-24 Approach,” IEEE CS International Conference on Information Technology and Computer Science, 13(03), 577–589. [Google Scholar] [Crossref]

38. Wang, Y., Chen, X., and Zhang, L. (2023). “Feature-fusion techniques for robust biometric authentication,” International Journal of Biometrics, 12(1), 23–37. [Google Scholar] [Crossref]

39. Xu Xiaona, Pan Xiuqin, Zhao Yue, and Pu Qiumei (2009). “Research on Kernel-Based Feature Fusion Algorithm in Multimodal Recognition,” IEEE CS International Conference on Information Technology and Computer Science, 3–6. [Google Scholar] [Crossref]

40. Xu, B., Huang, J.Z., Williams, G., Wang, Q., and Ye, Y. (2012). “Classifying very high-dimensional data with random forests built from small subspaces,” International Journal of Data Warehousing and Mining, 8, 44–63. [Google Scholar] [Crossref]

41. Zhang, L., Li, H., and Sun, Z. (2021). “Multimodal biometric fusion,” IEEE Transactions on Cybernetics, 51(3), 1450–1462. [Google Scholar] [Crossref]

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