Fingerprint retrieval by spatial modelling and distorted sample generation

In this study, the authors extend and refine the process of fingerprint retrieval, with the goal of boosting recognition rates for the first rank candidate and low penetration rates. On top of a baseline retrieval system which extracts Gabor features in multiple directions from fingerprint images, the authors propose spatial modelling techniques to generate artificial samples for training the system. Translational modelling, rotational modelling and distorted sample generation techniques are used to augment the original training set in order to boost the accuracy of fingerprint retrieval. The effectiveness of the models is evaluated using the well-known National Institute of Standards and Technology database 4. Experimental results, with reference to some leading fingerprint retrieval rates reported in the literature, confirm that the authors’ proposed system is promising in recognition performance.