Xuejun Tan

Fingerprint indexing based on novel features of minutiae triplets

We are concerned with accurate and efficient indexing of fingerprint images. We present a model-based approach, which efficiently retrieves correct hypotheses using novel features of triangles formed by the triplets of minutiae as the basic representation unit. The triangle features that we use are its angles, handedness, type, direction, and maximum side. Geometric constraints based on other characteristics of minutiae are used to eliminate false correspondences. Experimental results on live-scan fingerprint images of varying quality and NIST special database 4 (NIST-4) show that our indexing approach efficiently narrows down the number of candidate hypotheses in the presence of translation, rotation, scale, shear, occlusion, and clutter. We also perform scientific experiments to compare the performance of our approach with another prominent indexing approach and show that the performance of our approach is better for both the live scan database and the ink based database NIST-4.

Fingerprint matching by genetic algorithms

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. In this paper, we propose a fingerprint-matching approach based on genetic algorithms (GA), which tries to find the optimal transformation between two different fingerprints. In order to deal with low-quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design a fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.

Fingerprint classification based on learned features

In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3% and 91.6%, respectively, which compare favorably with other published research and are one of the best results published to date.

A Triplet Based Approach for Indexing of Fingerprint Database for Identification

This paper presents a model-based approach which efficiently retrieves correct hypotheses using properties of triangles formed by the triplets of minutiae as the basic representation unit. We show that the uncertainty of minutiae locations associated with feature extraction and shear does not affect the angles of a triangle arbitrarily. Geometric constraints based on characteristics of minutiae are used to eliminate erroneous correspondences. We present an analysis to characterize the discriminating power of our indexing approach. Experimental results on fingerprint images of varying quality show that our approach efficiently narrows down the number of candidate hypotheses in the presence of translation, rotation, scale, shear, occlusion and clutter.

Logical templates for feature extraction in fingerprint images

We present an approach for extraction of minutiae features from fingerprint images. The proposed approach is based on the use of logical templates for minutiae extraction in the presence of data distortion. A logical template is an expression that is applied to the binary ridge (valley) image at selected potential locations to detect the presence of minutiae at these locations. It is adapted to local ridge orientation and frequency. We discuss the proposed technique in detail, and present experimental results on low-resolution images of various qualities.

A robust two step approach for fingerprint identification

Due to the complex distortions involved in two impressions of the same finger, fingerprint identification is still a challenging problem. In this paper, we propose a two step fingerprint identification approach based on the triplets of minutiae. The features that we use to find the potential corresponding triangles include angles, triangle orientation, triangle direction, maximum side, minutiae density and ridge counts. In the first step, based on the number of corresponding triangles between the query fingerprint and the model database constructed offline, hypotheses are generated. In the second step, called verification, false corresponding triangles are eliminated by applying constraints to the transformation between two potential corresponding triangles. The experimental results on National Institute of Standards and Technology special fingerprint database 4, NIST-4, show that the proposed approach provides a reduction by a factor of 10 for the number of the hypotheses that need to be considered if linear search is used and can achieve a good performance even when a large portion of fingerprints in the database are of poor quality.

Fingerprint identification: classification vs. indexing

We present a comparison of two key approaches for fingerprint identification. These approaches are based on (a) classification followed by verification, and (b) indexing followed by verification. The fingerprint classification approach is based on a novel feature-learning algorithm. It learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. These features are then used for classification of fingerprints into five classes. The indexing approach is based on novel triplets of minutiae. The verification algorithm, based on least square minimization over each of the possible minutiae triplet pairs, is used for identification in both cases. On the NIST-4 fingerprint database, the comparison shows that, although correct classification rate can be as high as 92.8% for 5-class problems, the indexing approach performs better, based on the size of the search space and identification results.

Robust fingerprint identification

Due to the complex distortions involved in two impressions of the same finger, fingerprint identification is still a challenging problem for person authentication. In this paper, we propose a fingerprint identification approach based on the triplets of minutiae. The features that we use to find the potential corresponding triangles include angles, triangle orientation, triangle direction, maximum side, minutiae density and ridge counts. False corresponding triangles are eliminated by applying constraints to the transformation between two potential corresponding triangles. The experimental results on National Institute of Standards and Technology special fingerprint database 4, NIST-4, show that, as compared to the linear search, the proposed approach provides a reduction by a factor of 200 for the number of the hypotheses that need to be considered and it can achieve good performance even when a large portion of fingerprints in the database are of poor quality.

Learning features for fingerprint classification

In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses visually meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from NIST-4, the correct rates for 4 and 5-class classification are 93.2% and 91.2% respectively, which compare favorably and have advantages over the best results published to date.

Learned templates for feature extraction in fingerprint images

Most current techniques for minutiae extraction in fingerprint images utilize complex preprocessing and postprocessing. In this paper, we propose a new technique, based on the use of learned templates, which statistically characterize the minutiae. Templates are teamed from examples by optimizing a criterion function using Lagranges method. To detect the presence of minutiae in test images, templates are applied with appropriate orientations to the binary image only at selected potential minutia locations. Several performance measures, which evaluate the quality and quantity of extracted features and their impact on identification, are used to evaluate the significance of learned templates. The performance of the proposed approach is evaluated on two sets of fingerprint images: one is collected by an optical scanner and the other one is chosen from NIST special fingerprint database 4. The experimental results show that learned templates can improve both the features and the performance of the identification system.