Fingerprint Image Quality Estimation and its Application to Multi-Algorithm Verification

Recently, image quality awareness has been found to increase recognition rates and to supportdecisions in multimodal authentication systems significantly. Nevertheless, automatic quality assessmentis still an open issue, especially with regard to biometric authentication tasks. Here we analyze theorientation tensor of fingerprint images with a set of symmetry descriptors, in order to detect fingerprintimage quality impairments like noise, lack of structure, blur, etc. Allowed classes of local shapes area priori application information for the proposed quality measures, therefore no training or explicitimage reference information is required. Our quality assessment method is compared to an existingautomatic method and a human opinion in numerous experiments involving several public databases.Once the quality of an image is determined, it can be exploited in several ways, one of which is toadapt fusion parameters in a monomodal multi-algorithm environment, here a number of fingerprintrecognition systems. In this work, several trained and non-trained fusion schemes applied to the scoresof these matchers are compared. A Bayes-based strategy for combining experts with weights on theirpast performances, able to readapt to each identity claim based on the input quality is developed andevaluated. To show some of the advantages of quality-driven multi-algorithm fusion, such as boostingrecognition rates, increasing computational efficiency, etc., a novel cascade fusion and simple fusionrules are employed in comparison as well