A New Framework for Quality Assessment of High-Resolution Fingerprint Images

The quality assessment of sets of features extracted from patterns of epidermal ridges on our fingers is a biometric challenge problem with implications on questions concerning security, privacy and identity fraud. In this work, we introduced a new methodology to analyze the quality of high-resolution fingerprint images containing sets of fingerprint pores. Our approach takes into account the spatial interrelationship between the considered features and some basic transformations involving point process and anisotropic analysis. We proposed two new quality index algorithms following spatial and structural classes of analysis. These algorithms have proved to be effective as a performance predictor and as a filter excluding low-quality features in a recognition process. The experiments using error reject curves show that the proposed approaches outperform the state-of-the-art quality assessment algorithm for high-resolution fingerprint recognition, besides defining a new method for reconstructing their friction ridge phases in a very consistent way.