Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis 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 many algorithms discussed earlier to detect forged finger prints. However, they suffer to achieve higher performance in terms of security. In this paper, a region centric minutiae propagation measure (RCMPM) based approach. First, the finger print image is read and removes the noisy points by applying the multi level Gabor filters. The Gabor filter has been applied in multiple level which helps to remove the noise from finger print image. The enhanced image is converted into number of integral image. The integral images are generated by splitting the image into number of tiny images according to the size of window. From the integral image produced, the island, dot, enclosure, bifurcation features are extracted. Extracted features are framed as feature vector and used to estimate the RCMPM measure. Based on the RCMPM 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 accuracy of forged print detection has been improved and reduces the false classification ratio.