A feature level fusion fingerprint indexing approach based on MV and MCC using SVM classifier

Now a day, the biometric traits are widely used due to the growing demand for secure and reliable identification of an individual. Fingerprint is used by many fields like military for border control and government applications in India as the Aadhaar project, in Europe as the Visa Information System (VIS), in USA as the US-VISIT / IDENT which generally contain billions of samples of fingerprints. In this paper, we propose a fingerprint indexing scheme at feature level fusion based on Minutiae Vicinity and Minutiae Cylindrical Code and a support-vector-machine-based learning algorithm is used to train the system using the feature extracted which will improve the efficiency in seeking a candidate reference list from large scale biometric data databases, where the personality related with the input data is dictated by contrasting it with each and every entry in the database. This coordinating procedure is tedious and conceivably increments, the rate of wrong identification, hence we propose a new fingerprint indexing approach which would improve the performance of the system.