An Optimal Orientation Certainty Level Approach for Fingerprint Quality Estimation

Analyzing the quality of fingerprints in advance can be benefit for a fingerprint recognition system to improve its performance. Representative features for the quality assessment of fingerprint images from two existed types of capture devices are different. Orientation certainty level (OCL) is an effective method to extract image orientation feature. However it is not an effective estimation system to cooperate with the extracted features. In this paper, we explore the application of optimization theory, and support vector machine (SVM) in the field of image processing. Our proposed optimal orientation certainty level (OOCL) approach calculates the OCL for each block, extracts features from the optimal OCL system and uses the SVM classifier to determine whether an image should be accepted as an input to the recognition system. Experimental results show that the proposed OOCL method can improve the recognition rate than OCL method.