Inter region minutiae transitive measure based finger print analysis for forgery detection in health care systems

The problem of forgery detection has been well studied and the forged finger prints produces highly impacting results in the biometric based security systems. There are number of approaches available for the detection of forged finger prints. However, they suffer to achieve higher performance in terms of security. To improve the security performance, an inter region minutiae transitive measure (IRMT) based approach. The method reads the input finger print image and preprocesses the image to remove the noisy points by applying the multi-level Gabor filters. The enhanced image has been split into number of sectional images to produce regional image. From the regional image, the method extracts various features of finger print like island, dot, enclosure, bifurcation and edges. Extracted features have been converted into a feature vector which has been used to estimate the IRMT measure. Based on the IRMT measure, the presence of forged finger print has been identified and the same has been used to identify the region which has been modified. The proposed algorithm improves the performance of finger print analysis and reduces the false classification ratio.