Quality Induced Multiclassifier Fingerprint Verification Using Extended Feature Set
01 Jan 2014-Vol. 292, pp 231-254
TL;DR: A quality induced multi-classifier fingerprint verification algorithm that incorporates both level-2 and level-3 features and the experiments performed on 1000 ppi fingerprint databases show the effectiveness of the proposed algorithms.
Abstract: Automatic fingerprint verification systems use ridge flow patterns and general morphological information for broad classification and minutia features for verification. With the availability of high resolution fingerprint sensors, it is now feasible to capture more intricate features such as ridges, pores, permanent scars, and incipient ridges. These fine details are characterized as level-3 or extended features and play an important role in matching and improving the verification accuracy. The main objective of this research is to develop a quality induced multi-classifier fingerprint verification algorithm that incorporates both level-2 and level-3 features. A quality assessment algorithm is developed that uses Redundant Discrete Wavelet Transform to extract edge, noise and smoothness information in local regions and encodes into a quality vector. The feature extraction algorithm first registers the gallery and probe fingerprint images using a two-stage registration process. Then, a fast Mumford-Shah curve evolution algorithm is used to extract four level-3 features namely, pores, ridge contours, dots, and incipient ridges. Gallery and probe features are matched using Mahalanobis distance measure and quality based likelihood ratio approach. Further, the quality induced sum rule fusion algorithm is used to combine the match scores obtained from level-2 and level-3 features. The experiments performed on 1000 ppi (pixels per inch) fingerprint databases show the effectiveness of the proposed algorithms.
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23 Jan 2014
TL;DR: A comparative study of gender difference in African American fingerprint patterns was conducted using a nonparametric method based on the U test as mentioned in this paper, which showed that there is no significant gender difference between African American males and females at the 0.05 level of significance.
Abstract: The testing and frequency distribution analysis of African American fingerprint patterns (loop, whorl, and arch) was conducted. It was shown that loops are the most common, whorls are the second most common, and arches are the least common with a very small percentage (4.33%). Most loops are ulnar loops while only 4.47% loops are radial loops. Of the total arches, 61.54% arches are plain arches and 38.46% arches are tented arches. A comparative study of gender difference in African American fingerprint patterns was conducted using a non-parametric method based on the U test. The U test results show that there is no significant gender difference in fingerprint patterns between African American males and females at the 0.05 level of significance.
5 citations
Cites background from "Quality Induced Multiclassifier Fin..."
...Fingerprint friction ridge features are generally described in a hierarchical order at three different levels [6, 7]:...
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References
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TL;DR: A new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets is proposed, which can detect objects whose boundaries are not necessarily defined by the gradient.
Abstract: We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
10,404 citations
Book•
10 Mar 2005TL;DR: This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
Abstract: A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators
3,821 citations
TL;DR: This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level by combining three biometric modalities (face, fingerprint and hand geometry).
1,611 citations