A Better Matching Accuracy for Verification & Identification Using Biometric Features

Fingerprints are the most widely used biometric feature for person identification & verification in the field of biometric identification. Fingerprint possesâ two main types of features that are used for automatic fingerprint identification & verification (i) Ridge & furrow structure that forms a special pattern in the central region of finger print & (ii) Minutiae details associated with the local ridge & furrow structure. This paper Presents an approach to speed up the matching process by classifying the fingerprint pattern into different groups at the time of enrollment & improves finger print matching while matching the input template with stored template. And apart from that we have introduced spectral minutiae features and involved the singular point algorithms and as well as into feature reduction algorithms. With reduced features we can also achieve a fast minutiae based matching algorithm. The algorithm result indicates that this approach manages to speed up the matching effectively and therefore prove to be suitable for large database like forensic divisions.