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

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%).