An Effective Finger-Print Validation and Identification Using A-KAZE and SURF Algorithm

Abstract

The identification of users through their fingerprints has been widely used in various applications such as biometrics, border control identification, payment gateways and so on. But a proper fingerprint validation system remains a major issue in precise detection and takes more time to process the fingerprints. To overcome these fore mentioned issues, this manuscript introduced an effective fingerprint validation system using the data collected from FVC 2000 DB1, FVC 2002 DB1 and FVC 2004 datasets. The pre-processing is supported for image enhancement using CLAHE algorithm equalization and normalization. Additionally, the signal images are effectively extracted by applying the A-KAZE and SURF extracted algorithm which helps to reduce the time required for feature extraction and description of the identification process. At the final stage, random sample consensus (RANSAC) algorithm is applied to identify and validate the fingerprint effectively with better accuracy. The elapse time of the proposed approach is 3.09 s whereas the elapse time of the automatic fingerprint-based authentication framework is 4.03 s and minutiae triangulation technique is 3.55 s. Similarly, when the proposed approach is evaluated with FVC 2004 dataset, the elapse time is 5.20 ms whereas the existing scale-invariant feature transform (SIFT) detector obtained elapse time of 4.35 s.