Quality Induced Fingerprint Identification using Extended Feature Set
08 Dec 2008-pp 1-6
TL;DR: Experiments conducted on a high resolution fingerprint database containing rolled, slap and latent images indicate that the novel algorithm presented offers significant benefits for fast fingerprint identification.
Abstract: Automatic fingerprint identification systems use level-1 and level-2 features for fingerprint identification. However, forensic examiners utilize inherent level-3 details along with level-2 features. Existing level-3 feature extraction algorithms are computationally expensive to be used for identification. This paper presents a novel algorithm for fast level-3 feature extraction and identification. The algorithm starts with computing local image quality score using redundant discrete wavelet transform. A fast curve evolution algorithm is then used to extract four level-3 features namely, pores, ridge contours, dots, and incipient ridges. Along with level-1 and level-2 features, these level-3 features are used in a Delaunay triangulation based indexing algorithm. Finally, quality-based likelihood ratio is used to further improve the identification performance. Experiments conducted on a high resolution fingerprint database containing rolled, slap and latent images indicate that the algorithm offers significant benefits for fast fingerprint identification.
Citations
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01 Aug 2015
TL;DR: A fresh idea to construct the dictionary by region wise to correct the orientation field in latent image by reconstructing orientation field to enhance the latent fingerprint image is proposed.
Abstract: Latent Fingerprint Images have been extensively used by law enforcement agencies in investigating the crime spot and use the necessary information obtained as evidence to validate the criminal in Court. Although an important breakthrough in this direction has already been made in plain biometrics recognition, still identifying biometric such as Face in CCTV footage and Latent Fingerprint in uncontrolled, uncooperative, and hostile environment is an open research problem. Poor quality, lack of clarity, absence of proper mechanism make the latent fingerprint preprocessing problem one of the persistent and challenging problem to extract the reliable features. Dictionary based learning technique has given significant result, in contrast to conventional orientation field estimation methods by reconstructing orientation field to enhance the latent fingerprint image. Distorted orientation field is corrected using orientation patches of good quality fingerprint from region wise dictionary. This paper proposes a fresh idea to construct the dictionary by region wise to correct the orientation field in latent image. To verify the accuracy of enhanced image, a statistical observation has been done and got the promising results. This study concentrates on latent fingerprint preprocessing module towards reliable and efficient (optimal) Latent Fingerprint Identification.
6 citations
TL;DR: The goal of the proposed system is to improve the performance of a standard embedded AFAS in order to enable its employment in mobile devices architectures and show an interesting trade-off between used resources, authentication time, and accuracy rate.
Abstract: The way people access resources, data and services, is radically changing using modern mobile technologies. In this scenario, biometry is a good solution for security issues even if its performance is influenced by the acquired data quality. In this paper, a novel embedded automatic fingerprint authentication system (AFAS) for mobile users is described. The goal of the proposed system is to improve the performance of a standard embedded AFAS in order to enable its employment in mobile devices architectures. The system is focused on the quality evaluation of the raw acquired fingerprint, identifying areas of poor quality. Using this approach, no image enhancement process is needed after the fingerprint acquisition phase. The Agility RC2000 board has been used to prototype the embedded device. Due its different image resolution and quality, the experimental tests have been conducted on both PolyU and FVC2002 DB2-B free databases. Experimental results show an interesting trade-off between used resources, authentication time, and accuracy rate. The best achieved false acceptance rate (FAR) and false rejection rate (FRR) indexes are 0% and 6.25%, respectively. The elaboration time is 62.6 ms with a working frequency of 50 MHz.
5 citations
Cites methods from "Quality Induced Fingerprint Identif..."
...Successively, in [24] the authors extend this technique designing a local image quality assessment algorithm....
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...In [23, 24] the authors present an image quality assessment software technique for a novel fingerprint multimodal algorithm to provide high accuracy under nonideal conditions....
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01 Jan 2011
TL;DR: A survey of current fingerprint matching methods and technical achievement in this area includes a large number of papers covering the research aspects of system design and applications of fingerprint matching, image feature representation and extraction.
Abstract: Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition from large database is an open problem. Extracting features out of poor quality prints is the most challenging problem faced in this area. For that we need effective and efficient fingerprint matching algorithms that meet user requirements, to identify similarity. This paper gives a brief survey of current fingerprint matching methods and technical achievement in this area. The survey includes a large number of papers covering the research aspects of system design and applications of fingerprint matching, image feature representation and extraction. Furthermore future research directions are suggested.
5 citations
Dissertation•
01 Feb 2017
TL;DR: This thesis improves the understanding and performance of automated matching systems for forensic latent fingerprints and fingerphoto images by designing an automated latent fingerprint segmentation algorithm that segments the fingerprint regions from background by distinguishing between ridge and non-ridge patterns.
Abstract: Fingerprint recognition has evolved over the decades, providing innumerable applications for improving the modern day security. Based on the method of capture, fingerprints can be classified into four variants: inked, live-scan, latent, and fingerphoto. Extensive research has been undertaken for inked and live-scanned fingerprints. However, research on latent fingerprints and fingerphoto matching is still in nascent stages. These two capture methodologies are semi-controlled or uncontrolled which pose significant variations in the feature space and therefore warrant further exploration. The key research challenges involved in building an automated system for latent fingerprint and fingerphoto matching are as follows: (i) lack of publicly available large scale datasets with diverse variations to motivate reproducible research, (ii) segmentation of foreground regions from the complex background surface, and (iii) lack of robust feature models to represent the noisy and partial finger ridge information. Currently, there are limited end-to-end automated systems for latent fingerprint and fingerphoto matching. This thesis primarily focuses in contributing towards building a completely automated “lights-out" matching system for these two fingerprint variants. There are four contributions ranging from creating large databases to designing algorithms for segmentation and feature extraction for these two fingerprint variants. First, we create two benchmark datasets with diverse acquisition methods: (i) Multi-sensor Optical and Latent Fingerprint (MOLF) dataset containing 19,200 fingerprint images with large intraclass and capture variations and (ii) IIIT-D SmartPhone FingerPhoto Dataset version 2 (ISPFD-v2) containing 16,800 images from 300 classes captured under different environmental setup. The second contribution is designing an automated latent fingerprint segmentation algorithm that segments the fingerprint regions from background by distinguishing between ridge and non-ridge patterns. Latent fingerprint segmentation is usually affected by the texture of the surface and smudges are introduced during lifting. The proposed learning-based algorithm is generalizable and can accommodate for unseen texture noises. Further, a novel Spectral Image Validation and Verification based metric is proposed to measure the effect of the segmentation algorithm. Third, a minutiae extraction algorithm is proposed as a major contribution towards the “lights-out" latent fingerprint matching. A novel group (or class) sparsity based l2,1 regularization method is proposed to improve the unsupervised features learnt using stacked autoencoders and Restricted Boltzmann Machines. Latent fingerprint minutiae extraction is then posed as a binary classification problem to classify patches as minutia or non-minutia. To the best of our knowledge, this is the first algorithm in literature for automated minutia extraction from latent fingerprints. The fourth contribution is towards fingerphoto recognition, in which a novel end-to-end fingerphoto matching algorithm is proposed that is invariant to the environmental factors such as background noise, illumination variation, and camera resolution. The ridge-valley pattern present in a fingerphoto in not as distinct as VII a fingerprint image, thus making minutia extraction highly noisy. The matching pipeline consists of a segmentation technique to extract the fingerphoto region of interest from varying background, followed by enhancement to neutralize the illumination imbalance and increase the ridge valley contrast. For feature extraction, a deep scattering network based representation is introduced. The resultant fingerphoto features are robust and invariant to environmental variations. By addressing these challenging problems, this thesis improves the understanding and performance of automated matching systems for forensic latent fingerprints and fingerphoto images.
5 citations
Cites methods from "Quality Induced Fingerprint Identif..."
..., [92] proposed a method to combine pore and ridge features with minutiae for improved verification....
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01 Jan 2012
TL;DR: A survey of fingerprints matching methods is presented, among them minutiae based methods are widely used, and hybrid methods are used for more reliable matching with an additional computational cost.
Abstract: Fingerprint recognition is a method of biometric authentication that uses pattern recognition techniques based on fingerprints image of the individual. Fingerprint patterns are full of ridges and valleys and these structures provide essential information for matching and classification. The steps for fingerprint recognition include image acquisition, preprocessing, feature extraction and matching. A number of pattern recognition methods have been used to perform fingerprint matching. In this paper a survey of fingerprints matching methods are presented, they have been classified into approaches based on minutiae, image transform and hybrid approaches, among them minutiae based methods are widely used, and Hybrid methods are used for more reliable matching with an additional computational cost. Comparison and contrasting of all these methods reveals that a lot of emphasis is put into the design of accurate fingerprint features extractor to improve the classification accuracy.
4 citations
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
Additional excerpts
...Level-3 feature extraction algorithm employs level-set based curve evolution [19] which begins with the energy functional [5]...
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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
"Quality Induced Fingerprint Identif..." refers background or methods in this paper
...Existing automatic fingerprint identification systems (AFIS) use level1 features for classification and level-2 minutia features for identification [14]....
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...In literature, researchers have proposed several algorithms for fingerprint identification using level-2 features [14]....
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...Several identification approaches have been proposed by the researchers but none of them use level-3 features [2], [3], [9], [14], [17]....
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Book•
01 Aug 1981
TL;DR: This chapter discusses Graphics, Image Processing, and Pattern Recognition, and the Reconstruction techniques used in this program, as well as some of the problems faced in implementing this program.
Abstract: 1: Introduction.- 1.1 Graphics, Image Processing, and Pattern Recognition.- 1.2 Forms of Pictorial Data.- 1.2.1 Class 1: Full Gray Scale and Color Pictures.- 1.2.2 Class 2: Bilevel or "Few Color" pictures.- 1.2.3 Class 3: Continuous Curves and Lines.- 1.2.4 Class 4: Points or Polygons.- 1.3 Pictorial Input.- 1.4 Display Devices.- 1.5 Vector Graphics.- 1.6 Raster Graphics.- 1.7 Common Primitive Graphic Instructions.- 1.8 Comparison of Vector and Raster Graphics.- 1.9 Pictorial Editor.- 1.10 Pictorial Transformations.- 1.11 Algorithm Notation.- 1.12 A Few Words on Complexity.- 1.13 Bibliographical Notes.- 1.14 Relevant Literature.- 1.15 Problems.- 2: Digitization of Gray Scale Images.- 2.1 Introduction.- 2.2 A Review of Fourier and other Transforms.- 2.3 Sampling.- 2.3.1 One-dimensional Sampling.- 2.3.2 Two-dimensional Sampling.- 2.4 Aliasing.- 2.5 Quantization.- 2.6 Bibliographical Notes.- 2.7 Relevant Literature.- 2.8 Problems.- Appendix 2.A: Fast Fourier Transform.- 3: Processing of Gray Scale Images.- 3.1 Introduction.- 3.2 Histogram and Histogram Equalization.- 3.3 Co-occurrence Matrices.- 3.4 Linear Image Filtering.- 3.5 Nonlinear Image Filtering.- 3.5.1 Directional Filters.- 3.5.2 Two-part Filters.- 3.5.3 Functional Approximation Filters.- 3.6 Bibliographical Notes.- 3.7 Relevant Literature.- 3.8 Problems.- 4: Segmentation.- 4.1 Introduction.- 4.2 Thresholding.- 4.3 Edge Detection.- 4.4 Segmentation by Region Growing.- 4.4.1 Segmentation by Average Brightness Level.- 4.4.2 Other Uniformity Criteria.- 4.5 Bibliographical Notes.- 4.6 Relevant Literature.- 4.7 Problems.- 5: Projections.- 5.1 Introduction.- 5.2 Introduction to Reconstruction Techniques.- 5.3 A Class of Reconstruction Algorithms.- 5.4 Projections for Shape Analysis.- 5.5 Bibliographical Notes.- 5.6 Relevant Literature.- 5.7 Problems.- Appendix 5.A: An Elementary Reconstruction Program.- 6: Data Structures.- 6.1 Introduction.- 6.2 Graph Traversal Algorithms.- 6.3 Paging.- 6.4 Pyramids or Quad Trees.- 6.4.1 Creating a Quad Tree.- 6.4.2 Reconstructing an Image from a Quad Tree.- 6.4.3 Image Compaction with a Quad Tree.- 6.5 Binary Image Trees.- 6.6 Split-and-Merge Algorithms.- 6.7 Line Encodings and the Line Adjacency Graph.- 6.8 Region Encodings and the Region Adjacency Graph.- 6.9 Iconic Representations.- 6.10 Data Structures for Displays.- 6.11 Bibliographical Notes.- 6.12 Relevant Literature.- 6.13 Problems.- Appendix 6.A: Introduction to Graphs.- 7: Bilevel Pictures.- 7.1 Introduction.- 7.2 Sampling and Topology.- 7.3 Elements of Discrete Geometry.- 7.4 A Sampling Theorem for Class 2 Pictures.- 7.5 Contour Tracing.- 7.5.1 Tracing of a Single Contour.- 7.5.2 Traversal of All the Contours of a Region.- 7.6 Curves and Lines on a Discrete Grid.- 7.6.1 When a Set of Pixels is not a Curve.- 7.6.2 When a Set of Pixels is a Curve.- 7.7 Multiple Pixels.- 7.8 An Introduction to Shape Analysis.- 7.9 Bibliographical Notes.- 7.10 Relevant Literature.- 7.11 Problems.- 8: Contour Filling.- 8.1 Introduction.- 8.2 Edge Filling.- 8.3 Contour Filling by Parity Check.- 8.3.1 Proof of Correctness of Algorithm 8.3.- 8.3.2 Implementation of a Parity Check Algorithm.- 8.4 Contour Filling by Connectivity.- 8.4.1 Recursive Connectivity Filling.- 8.4.2 Nonrecursive Connectivity Filling.- 8.4.3 Procedures used for Connectivity Filling.- 8.4.4 Description of the Main Algorithm.- 8.5 Comparisons and Combinations.- 8.6 Bibliographical Notes.- 8.7 Relevant Literature.- 8.8 Problems.- 9: Thinning Algorithms.- 9.1 Introduction.- 9.2 Classical Thinning Algorithms.- 9.3 Asynchronous Thinning Algorithms.- 9.4 Implementation of an Asynchronous Thinning Algorithm.- 9.5 A Quick Thinning Algorithm.- 9.6 Structural Shape Analysis.- 9.7 Transformation of Bilevel Images into Line Drawings.- 9.8 Bibliographical Notes.- 9.9 Relevant Literature.- 9.10 Problems.- 10: Curve Fitting and Curve Displaying.- 10.1 Introduction.- 10.2 Polynomial Interpolation.- 10.3 Bezier Polynomials.- 10.4 Computation of Bezier Polynomials.- 10.5 Some Properties of Bezier Polynomials.- 10.6 Circular Arcs.- 10.7 Display of Lines and Curves.- 10.7.1 Display of Curves through Differential Equations.- 10.7.2 Effect of Round-off Errors in Displays.- 10.8 A Point Editor.- 10.8.1 A Data Structure for a Point Editor.- 10.8.2 Input and Output for a Point Editor.- 10.9 Bibliographical Notes.- 10.10 Relevant Literature.- 10.11 Problems.- 11: Curve Fitting with Splines.- 11.1 Introduction.- 11.2 Fundamental Definitions.- 11.3 B-Splines.- 11.4 Computation with B-Splines.- 11.5 Interpolating B-Splines.- 11.6 B-Splines in Graphics.- 11.7 Shape Description and B-splines.- 11.8 Bibliographical Notes.- 11.9 Relevant Literature.- 11.10 Problems.- 12: Approximation of Curves.- 12.1 Introduction.- 12.2 Integral Square Error Approximation.- 12.3 Approximation Using B-Splines.- 12.4 Approximation by Splines with Variable Breakpoints.- 12.5 Polygonal Approximations.- 12.5.1 A Suboptimal Line Fitting Algorithm.- 12.5.2 A Simple Polygon Fitting Algorithm.- 12.5.3 Properties of Algorithm 12.2.- 12.6 Applications of Curve Approximation in Graphics.- 12.6.1 Handling of Groups of Points by a Point Editor.- 12.6.2 Finding Some Simple Approximating Curves.- 12.7 Bibliographical Notes.- 12.8 Relevant Literature.- 12.9 Problems.- 13: Surface Fitting and Surface Displaying.- 13.1 Introduction.- 13.2 Some Simple Properties of Surfaces.- 13.3 Singular Points of a Surface.- 13.4 Linear and Bilinear Interpolating Surface Patches.- 13.5 Lofted Surfaces.- 13.6 Coons Surfaces.- 13.7 Guided Surfaces.- 13.7.1 Bezier Surfaces.- 13.7.2 B-Spline Surfaces.- 13.8 The Choice of a Surface Partition.- 13.9 Display of Surfaces and Shading.- 13.10 Bibliographical Notes.- 13.11 Relevant Literature.- 13.12 Problems.- 14: The Mathematics of Two-Dimensional Graphics.- 14.1 Introduction.- 14.2 Two-Dimensional Transformations.- 14.3 Homogeneous Coordinates.- 14.3.1 Equation of a Line Defined by Two Points.- 14.3.2 Coordinates of a Point Defined as the Intersection of Two Lines.- 14.3.3 Duality.- 14.4 Line Segment Problems.- 14.4.1 Position of a Point with respect to a Line.- 14.4.2 Intersection of Line Segments.- 14.4.3 Position of a Point with respect to a Polygon.- 14.4.4 Segment Shadow.- 14.5 Bibliographical Notes.- 14.6 Relevant Literature.- 14.7 Problems.- 15: Polygon Clipping.- 15.1 Introduction.- 15.2 Clipping a Line Segment by a Convex Polygon.- 15.3 Clipping a Line Segment by a Regular Rectangle.- 15.4 Clipping an Arbitrary Polygon by a Line.- 15.5 Intersection of Two Polygons.- 15.6 Efficient Polygon Intersection.- 15.7 Bibliographical Notes.- 15.8 Relevant Literature.- 15.9 Problems.- 16: The Mathematics of Three-Dimensional Graphics.- 16.1 Introduction.- 16.2 Homogeneous Coordinates.- 16.2.1 Position of a Point with respect to a Plane.- 16.2.2 Intersection of Triangles.- 16.3 Three-Dimensional Transformations.- 16.3.1 Mathematical Preliminaries.- 16.3.2 Rotation around an Axis through the Origin.- 16.4 Orthogonal Projections.- 16.5 Perspective Projections.- 16.6 Bibliographical Notes.- 16.7 Relevant Literature.- 16.8 Problems.- 17: Creating Three-Dimensional Graphic Displays.- 17.1 Introduction.- 17.2 The Hidden Line and Hidden Surface Problems.- 17.2.1 Surface Shadow.- 17.2.2 Approaches to the Visibility Problem.- 17.2.3 Single Convex Object Visibility.- 17.3 A Quad Tree Visibility Algorithm.- 17.4 A Raster Line Scan Visibility Algorithm.- 17.5 Coherence.- 17.6 Nonlinear Object Descriptions.- 17.7 Making a Natural Looking Display.- 17.8 Bibliographical Notes.- 17.9 Relevant Literature.- 17.10 Problems.- Author Index.- Algorithm Index.
1,395 citations
"Quality Induced Fingerprint Identif..." refers methods in this paper
...Once the contour is obtained, curve tracing [16] is used to...
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Book•
01 Jan 2006
TL;DR: Details multi-modal biometrics and its exceptional utility for increasingly reliable human recognition systems and the substantial advantages of multimodal systems over conventional identification methods.
Abstract: Details multimodal biometrics and its exceptional utility for increasingly reliable human recognition systems. Reveals the substantial advantages of multimodal systems over conventional identification methods.
1,068 citations
"Quality Induced Fingerprint Identif..." refers methods in this paper
...Finally, the quality score, qk, is normalized in the range of [0,1] using min-max normalization [18]....
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