Securing biometrics databases from being compromised is one of the most important challenges that must be overcome in order to demonstrate the viability of biometrics based authentication. In this paper we present a novel method of hashing fingerprint minutia and performing fingerprint identification in the hash space. Our approach uses a family of symmetric hash functions and does not depend on the location of the (usually unstable) singular points (core and delta). In fact, most approaches of hashing minutia and developing a cancellable system described in the literature assume the location of the singular points. Others assume a pre-alignment between the test and the stored fingerprint templates. These assumptions are unrealistic given that fingerprints are very often only partially captured by the commercially available sensors. The Equal Error Rate (EER) achieved by our system is about 3%. We also present the performance analysis of a hybrid system that has an EER of about 2% which is very close to the performance of plain matching in the minutia space.

No abstract

To compensate for the different orientations of two fingerprint images, matching systems use a reference point and a set of transformation parameters. Fingerprint minutiae are compared on their positions relative to the reference points, using a set of thresholds for the various matching features. However a pair of minutiae might have similar values for some of the features compensated by dissimilar values for others; this tradeoff cannot be modeled by arbitrary thresholds, and might lead to a number of false matches. Instead given a list of potential correspondences of minutiae points, we could use a static classifier, such as a support vector machine (SVM) to eliminate some of the false matches. A 2-class model is built using sets of minutiae correspondences from fingerprint pairs known to belong to the same and different users. For a test pair of fingerprints, a similar set of minutiae correspondences is extracted and given to the recognizer, using only those classified as genuine matches to calculate the similarity score, and thus, the matching result. We have built recognizers using different combinations of fingerprint features and have tested them against the FVC 2002 database. Using this recognizer reduces the number of false minutiae matches by 19%, while only 5% of the minutiae pairs corresponding to fingerprints of the same user are rejected. We study the effect of such a reduction on the final error rate, using different scoring schemes.

Rather than use arbitrary matching threshold values and a heuristic set of features while comparing minutiae points during the fingerprint verification process, we develop a system which considers only the optimal features, which contain the highest discriminative power, from a predefined feature set. For this, we use a feature selection algorithm which adds features, one at a time, till it arrives at an optimal feature set of the target size. The classifier is trained on this feature set, on a two class problem representing pairs of matched minutiae points belonging to fingerprints of same and different users. During the test phase, the system generates a number of candidate matched minutiae pairs; features from each of them are extracted and given to the classifier. Those that are incorrectly matched are eliminated from the scoring algorithm. We have developed a set of seven candidate features, and tested our system using the FVC 2002 DB1 fingerprint database. We study how feature sets of different sizes affect the accuracy of the system, and observe how additional features not necessarily would improve the performance of a classifier. This is illustrated in how using a 3 feature set gives us the most accurate system and using bigger feature sets cause a slight drop in accuracy.

Class syntax can be used to 1) model temporal or locational evolvement of class labels of feature observation sequences, 2) correct classification errors of static classifiers if feature observations from different classes overlap in feature space, and 3) eliminate redundant features whose discriminative information is already represented in the class syntax. In this paper, we describe a novel method that combines static classifiers with class syntax models for supervised feature subset selection and classification in unified algorithms. Posterior class probabilities given feature observations are first estimated from the output of static classifiers, and then integrated into a parsing algorithm to find an optimal class label sequence for the given feature observation sequence. Finally, both static classifiers and class syntax models are used to search for an optimal subset of features. An optimal feature subset, associated static classifiers, and class syntax models are all learned from training data. We apply this method to logical entity recognition in scanned historical U.S. Food and Drug Administration (FDA) documents containing court case Notices of Judgments (NJs) of different layout styles, and show that the use of class syntax models not only corrects most classification errors of static classifiers, but also significantly reduces the dimensionality of feature observations with negligible impact on classification performance.