Dissertation•
Fingerprint Image Quality: Predicting Biometric Performance
30 Nov 2015-
TL;DR: This work provides comprehensive algorithm descriptions and makes available implementations of adaptations of 10 quality assessment algorithms from the literature which operate at the local or global image level.
Abstract: Finger image quality assessment is a crucial part of any system where a high biometric performance and user satisfaction is desired. Several algorithms measuring selected aspects of finger image quality have been proposed in the literature, yet only few of them have found their way into quality assessment algorithms used in practice. We provide comprehensive algorithm descriptions and make available implementations of adaptations of 10 quality assessment algorithms from the literature which operate at the local or global image level. We evaluate the performance on four datasets in terms of the capability in determining samples causing false non-matches and by their Spearman correlation with sample utility. Our evaluation shows that both the capability in rejecting samples causing false non-matches and the correlation between features varies depending on the dataset. 4.
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01 Jan 2016
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2,687 citations
DOI•
01 Jan 2019
TL;DR: This book summarizes the findings of a two-year review of the United Nations’ refugee policy in Syria and makes a number of recommendations about how to proceed from there.
Abstract: .............................................................................................................. 5 Acknowledgements ................................................................................................ 6 Executive Summary ............................................................................................... 8 Policy Context ................................................................................................... 8 Key Conclusions .............................................................................................. 10 List of recommendations................................................................................... 1
19 citations
TL;DR: The Spectral Image Validation/Verification (SIVV) utility differentiates fingerprints from non-fingerprints, including blank frames or segmentation failures erroneously included in data; provides a "first look" at image quality; and can identify anomalies in sample rates of scanned images.
Abstract: Integrity of fingerprint data is essential to biometric and forensic applications. Accordingly, the FBI's Criminal Justice
Information Services (CJIS) Division has sponsored development of software tools to facilitate quality control functions
relative to maintaining its fingerprint data assets inherent to the Integrated Automated Fingerprint Identification System
(IAFIS) and Next Generation Identification (NGI). This paper provides an introduction of two such tools. The first FBI-sponsored
tool was developed by the National Institute of Standards and Technology (NIST) and examines and detects
the spectral signature of the ridge-flow structure characteristic of friction ridge skin. The Spectral Image
Validation/Verification (SIVV) utility differentiates fingerprints from non-fingerprints, including blank frames or
segmentation failures erroneously included in data; provides a "first look" at image quality; and can identify anomalies
in sample rates of scanned images. The SIVV utility might detect errors in individual 10-print fingerprints inaccurately
segmented from the flat, multi-finger image acquired by one of the automated collection systems increasing in
availability and usage. In such cases, the lost fingerprint can be recovered by re-segmentation from the now compressed
multi-finger image record. The second FBI-sponsored tool, CropCoeff was developed by MITRE and thoroughly tested
via NIST. CropCoeff enables cropping of the replacement single print directly from the compressed data file, thus
avoiding decompression and recompression of images that might degrade fingerprint features necessary for matching.
4 citations
References
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Journal Article•
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
272,030 citations
"Fingerprint Image Quality: Predicti..." refers methods in this paper
...Experiments were performed using R [207] with SVM from e1071 [208]; POLR from MASS [209]; cross validation and miscellaneous functions from caret [210] and xtable [211] packages....
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TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
"Fingerprint Image Quality: Predicti..." refers background or methods in this paper
...The histogram feature vectors are used for training a supervised Random Forest (RF)[164] model to classify them into five quality classes, determined by the produced comparison scores....
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...3 Random Forests Random Forests (RF) was introduced by Breiman[164] as an ensemble machine learning method for supervised classification and regression....
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...Random Forests [164] basically grows many classification trees....
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...Our proposed method is based on a combination of unsupervised (selforganizing map [163]) and supervised (Random Forest [164]) machine learning algorithm....
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...The interpretation of these features to quality scores is done by four different approaches using three different machine learning techniques - SOM, Generative Topographic Mapping (GTM)[173] and Random Forests (RF)[164] with raw features or histogram based feature vectors....
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49,597 citations
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
37,861 citations
"Fingerprint Image Quality: Predicti..." refers background or methods in this paper
...We train our predictive models using Multi-class Support Vector Machine (SVM) [45] and Proportional Odds Logistic Regression (POLR) [206] where the response variable is the assigned quality level (either individual examiner or median of examiners), and the explanatory variables are features in sets A or B (see section 12....
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...13, 18, 19, 103 SVM Support Vector Machine [45]....
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...We further grouped the quantized comparison scores (which are in [1-100]) into 5 levels such that quantized scores in [1, 20] belongs to bin 1, [21, 40] belongs to bin 2 and so on....
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...A learning based approach using Support Vector Machine [45] (SVM) was proposed by Bazen and Gerez [46] and extended by Yin et al....
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TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Abstract: CONSIDER Table 1. It represents in its formal characteristics a situation which arises in the clinical-social-personality areas of psychology, where it frequently occurs that the only useful level of measurement obtainable is nominal scaling (Stevens, 1951, pp. 2526), i.e. placement in a set of k unordered categories. Because the categorizing of the units is a consequence of some complex judgment process performed by a &dquo;two-legged meter&dquo; (Stevens, 1958), it becomes important to determine the extent to which these judgments are reproducible, i.e., reliable. The procedure which suggests itself is that of having two (or more) judges independently categorize a sample of units and determine the degree, significance, and
34,965 citations
"Fingerprint Image Quality: Predicti..." refers methods in this paper
...Other coefficients like Cohens Kappa [202] or Fleiss Kappa [203] were designed to measure inter-rater agreement over the whole assessment population which is expressed by the arithmetic mean of the set of CMCA....
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...The predictive capability of the constructed models in each experiment setting is determined by calculating the mean and standard deviation of the F1 score and Cohen’s Kappa (κ) [202] over the 10 permutations....
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