Fingerprint preselection using eigenfeatures

In this paper we propose a new distance measure for an identification problem and describe experiments on fingerprint preselection using eigenfeatures of ridge direction patterns. The distance is defined by likelihood ratio of error distribution of feature vectors to the whole distribution of feature vector differences. In addition, we introduce “quality indexes” of feature vectors and make the distance adaptive to the quality indexes. Experiments on fingerprint preselection for ten-print cards revealed that our proposed distance is much more effective than the Mahalanobis distance. By combining the eigenfeatures and traditional classification features, 0.06% false acceptance rate at 2.0% false rejection rate and one million cards/sec preselection speed on a standard workstation have been achieved. This makes it possible to construct high performance fingerprint identification systems