Score and Feature Level Fusion Approaches for Evaluation of Multi-Features of Fingerprint Modality for Person Recognition System

Abstract

In the biometrics, the technologies grow day by day and the security also increased related to that technologies. The fingerprint was the most intensively researched in the field of biometrics system due to permanence and uniqueness features which made varies of different peoples. The paper addressing many stages, in addition to the primary stages of any biometrics system the fusion of unimodal system was used in order to improve the performance of the system. The double enhancement techniques were used to make the images very clear by Histogram Equalization and Fast Fourier Transformation (FFT). The feature extraction was conducted using three techniques which called Zernike Moment (ZM), Hu-Moments (Hu) and Gray-Level Co-occurrence Matrix (GLCM) that categorized to statistical and texture features. The matching between these features was performed using the Euclidean distance to find the scores matrix. Additionally, the fusion as the most modern technique was used to improve the performance of the biometrics system which performed in this work by feature level and score level fusions. The feature level fusion by using concatenation and score level fusion by using Weight sum rule strategy led to improve the performance of the system. The system was evaluated by False Accepted Rate (FAR), False Rejected Rate (FRR), Equal Error Rate (EER) and Genuine Accept Rate (GAR). The results show that, the fusion gave the most efficiency results compared with individual system. The work was tested on four datasets such as Fingerprint Verification Competition (FVC2000), (FVC2002), (FVC2004) and our department datasets which called KVK dataset. The best results were achieved by FVC2002 with maximum GAR reached to 98.45% and minimum EER of 1.54% as compared with other datasets and existing works.