Adaptive Quality Enhancement Fingerprint Analysis

Abstract

Poor quality of the fingerprint image prevents accurate recognition as the employed methods are largely dependent on the fingerprint image quality. Algorithms will be better suited to detect fingerprint images if they are adapted according to their quality classes. In this paper, a class adaptive fingerprint enhancement algorithm is presented, classes dry, good and wet are assigned and further image processing is carried out. Features such as mean, variance, moisture index, Ridge Valley Area Uniformity (RVAU) are extracted from the ROI images. There are two stages of fingerprint quality enhancements which include the quality preprocessing (QP) and the enhancement stage. Support Vector Machine (SVM) algorithm is used to classify the images. Further, comparison scores are calculated by comparing the given image with the database of the minutiae using the minutiae matching technique. Experimentation is carried out on the FVC fingerprint database. A comparative analysis of the fuzzy C-means based clustering and mean based clustering is also experimented.