Analysis of Score Level Fusion of Biometric Features

Abstract

Biometric systems have gained acceptance in a variety of industries in recent years, and they continue to improve security features for access control systems. Numerous types of monotonic biometric systems have been developed. On the other hand, these systems can only provide low- to mid-level security features. As a result, combining two or more collinear biometrics are necessitated for significantly greater functionality. In this paper, a multimodal biometric technology for iris, face, and fingerprint assimilation has offered. Here, an effective matching approach based on Principal Component Analysis that employs three biometric modes to solve this challenge: iris, face, and fingerprint. The three modalities are integrated at the score level fusion, and fusion is conducted. Here, authors have proposed a combination of Iris-Fingerprint, Iris-Face, and Face-Fingerprint to develop the model. Statistical parameters like True positive (TP), True Negative, False positive, False Negative, F1 score, Accuracy are tested for different threshold values. For Iris-Fingerprint, Iris-Face, and Face-Fingerprint, our suggested technique yields accuracy of 79 percent, 85 percent, and 82 percent, respectively. Finally, a ROC curve was created using a Linear Support Vector Machine for all of the combinations, with an Area under Curve of 0.83 for the fusion of Iris and Face.