Fingerprint Image Enhancement Using Orientation Mapping

Now a days security issues are one of the major concerns that arise due to the weaknessof passwords or illegal hallmark. At one end, there has been a continuous and enormousimprovement in the human’s lifestyle while at the other end; the technological crimes areincreasing rapidly. We see problem and solution at two sides of the same coin of human’s life.The need for a compromising technology which can be adopted is highly imperative. The onlyknock-down solution for the problem of illegal authentication is Biometrics. It is a means ofusing the physiological or behavioral characteristics of a person as a kind of permanentpassword. Fingerprint can play a vital role as an ambassador in biometric technology. It is themost popular and one of the most accurate and cost effective solutions.In practice, due to skin conditions, impression conditions, ridge configuration, and theproblems of acquisition devices etc., a large amount of acquired fingerprint images are of poorquality. In order to ensure that the performance of the minutiae feature extraction algorithmwill be robust with respect to the quality of input fingerprint image, an enhancementalgorithm which can improve the clarity of the ridge and valley structures is necessary.Fingerprints have redundant information (not just a single ridge, but multiple ridgesgoing in the same direction, in small neighborhoods.) This information is used forenhancement, prior to minutiae extraction.This paper proposed a geometric knowledge basedapproach on fingerprint image enhancement process. We have focused on local ridgesorientation. We have computed ridge orientation using local ridges orientation mapping. Thenwe have compared with geometric analysis. Our proposed algorithm concluded with fourmajor parts: normalization, orientation mapping, filtering, and Binarization. The performanceof the algorithm has been evaluated on high, medium, and low fingerprint images.