De-Duplication Complexity of Fingerprint Data in Large-Scale Applications

De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population. De-duplication is the process of removing the instances of multiple enrollments by the same person using the person's biometric data. An important issue in the large-scale de-duplication applications is the speed of matching and the accuracy of the matching because the number of persons to be enrolled runs into millions. This paper presents an efficient method to improve the accuracy of fingerprint de-duplication in de-centralized manner. De-duplication accuracy decreases because of the noise present in the data, which would cause improper slap fingerprint segmentation. In this paper, an attempt is made to remove the noise present in the data by using binarization of slap fingerprint images and region labeling of desired regions with 8-adjacency neighborhood. The distinct feature of this technique is to remove the noise present in the data for an accurate slap fingerprint segmentation and improve the de-duplication accuracy. Experimental results demonstrate that the fingerprint segmentation rate and de-duplication accuracy are improved significantly.