Rule-Based Fingerprint Quality Estimation System Using the Optimal Orientation Certainty Level Approach

The fingerprint quality can be used as a good predictor for fingerprint recognition performance. Knowing the fingerprint quality in advance is useful to improve the performance of fingerprint recognition system. In this paper, we propose an effective quality estimation system with 4 rules which are applied in two-step. Each rule consists of a Back-Propagation Neural Networks (BPNN) classifier based on Optimized Orientation Certainty Level (OOCL) features extracted locally from fingerprint images. Experimental results show that the proposed two-step OOCL method can estimate fingerprint quality more effectively.