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E-raamat: Touchless Fingerprint Biometrics

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Offering the first comprehensive analysis of touchless fingerprint-recognition technologies,Touchless Fingerprint Biometrics gives an overview of the state of the art and describes relevant industrial applications. It also presents new techniques to efficiently and effectively implement advanced solutions based on touchless fingerprinting.

The most accurate current biometric technologies in touch-based fingerprint-recognition systems require a relatively high level of user cooperation to acquire samples of the concerned biometric trait. With the potential for reduced constraints, reduced hardware costs, quicker acquisition time, wider usability, and increased user acceptability, this book argues for the potential superiority of touchless biometrics over touch-based methods.

The book considers current problems in developing high-accuracy touchless recognition technology. It discusses factors such as shadows, reflections, complex backgrounds, distortions due to perspective effects, uncontrolled finger placement, inconstant resolution of the ridge pattern, and reconstruction and processing of three-dimensional models. The last section suggests what future work can be done to increase accuracy in touchless systems, such as intensive studies on extraction and matching methods and three-dimensional analytical capabilities within systems.

In a world where usability and mobility have increasing relevance, Touchless Fingerprint Biometrics demonstrates that touchless technologies are also part of the future. A presentation of the state of the art, it introduces you to the field and its immediate future directions.

Preface xi
Acknowledgments xiii
Authors xv
1 Introduction 1(6)
1.1 State of the Art
3(1)
1.2 The Performed Research
3(1)
1.3 Results
4(1)
1.4 Structure of This Book
5(2)
2 Biometric Systems 7(18)
2.1 Biometric Traits
7(3)
2.2 Applications
10(3)
2.3 Evaluation of Biometric Systems
13(9)
2.3.1 Evaluation Strategies
14(1)
2.3.2 Evaluation Aspects
14(2)
2.3.3 Accuracy Evaluation
16(5)
2.3.3.1 Methods for Accuracy Evaluation
16(2)
2.3.3.2 Accuracy Indexes
18(3)
2.3.4 Confidence Estimation
21(1)
2.4 Research Trends
22(1)
2.5 Summary
23(2)
3 Touchless and Less-Constrained Biometrics 25(12)
3.1 Less-Constrained Biometric Systems
26(1)
3.2 Touchless Biometric Traits
27(6)
3.2.1 Less-Constrained Face Recognition
27(3)
3.2.2 Less-Constrained Iris Recognition
30(2)
3.2.3 Soft Biometrics
32(1)
3.2.4 Other Biometric Traits
33(1)
3.3 Touch-Based Biometric Traits
33(1)
3.4 Summary
34(3)
4 Fingerprint Biometrics 37(46)
4.1 Fingerprint Recognition
37(2)
4.2 Characteristics of the Fingerprint
39(1)
4.3 Applications
39(1)
4.4 Analysis of Fingerprint Samples
40(7)
4.4.1 Level 1 Analysis
40(3)
4.4.2 Level 2 Analysis
43(3)
4.4.3 Level 3 Analysis
46(1)
4.5 Touch-Based Fingerprint Recognition
47(13)
4.5.1 Acquisition and Fingerprint Images
48(2)
4.5.2 Quality Estimation of Fingerprint Samples
50(1)
4.5.3 Image Enhancement
51(2)
4.5.4 Feature Extraction and Matching
53(5)
4.5.4.1 Correlation-Based Techniques
53(1)
4.5.4.2 Minutiae-Based Methods
54(3)
4.5.4.3 Other Feature-Based Methods
57(1)
4.5.5 Fingerprint Classification and Indexing
58(1)
4.5.6 Computation of Synthetic Fingerprint Images
59(1)
4.6 Touchless Fingerprint Biometrics
60(19)
4.6.1 Fingerprint Recognition Based on Touchless Two-Dimensional Samples
62(7)
4.6.1.1 Acquisition
63(2)
4.6.1.2 Computation of a Touch-Equivalent Image
65(1)
4.6.1.3 Touch-Equivalent Samples from Multiple Images
66(2)
4.6.1.4 Feature Extraction and Matching
68(1)
4.6.2 Fingerprint Recognition Based on Touchless Three-Dimensional Samples
69(8)
4.6.2.1 Acquisition
70(4)
4.6.2.2 Computation of a Touch-Equivalent Image
74(2)
4.6.2.3 Feature Extraction and Matching
76(1)
4.6.3 Applications of Touchless Fingerprint Biometrics
77(2)
4.7 Summary
79(4)
5 Touchless Fingerprint Recognition 83(54)
5.1 Touchless Fingerprint Recognition Techniques
83(3)
5.2 Methods Based on Two-Dimensional Samples
86(20)
5.2.1 Acquisition
87(1)
5.2.2 Quality Assessment of Touchless Fingerprint Images
87(6)
5.2.2.1 Computation of the ROI
89(1)
5.2.2.2 Quality Assessment Based on Computational Intelligence Classifiers (Method QA)
89(3)
5.2.2.3 Quality Assessment Based on Techniques Described in the Literature (Method QB)
92(1)
5.2.3 Computation of Touch-Equivalent Images
93(2)
5.2.3.1 Enhancement Based on Contextual Filters (Method EA)
93(2)
5.2.3.2 Enhancement Based on the Ridge-Following Approach (Method EB)
95(1)
5.2.3.3 Resolution Normalization
95(1)
5.2.4 Analysis of Level 1 Features in Touchless Fingerprint Images
95(4)
5.2.4.1 Singular Region Estimation
97(1)
5.2.4.2 Feature Extraction
98(1)
5.2.4.3 Core Estimation Using Computational Intelligence Techniques
99(1)
5.2.5 Analysis of Level 2 Features in Touchless Fingerprint Images
99(1)
5.2.6 Reduction of Perspective and Rotation Effects
100(6)
5.2.6.1 Image Preprocessing
101(1)
5.2.6.2 Finger Rotation Simulation
102(1)
5.2.6.3 Feature Extraction
103(2)
5.2.6.4 Rotation Estimation with Neural Networks
105(1)
5.2.6.5 Template Computation
106(1)
5.3 Methods Based on Three-Dimensional Models
106(20)
5.3.1 Three-Dimensional Reconstruction of Minutiae Points
106(5)
5.3.1.1 Acquisition, Image Preprocessing, and Minutiae Estimation
107(2)
5.3.1.2 Computation of Features Related to Each Minutia
109(1)
5.3.1.3 Classification of Minutiae Pairs
110(1)
5.3.1.4 Three-Dimensional Reconstruction of Minutiae Pairs
110(1)
5.3.2 Three-Dimensional Reconstruction of the Finger Surface
111(9)
5.3.2.1 Camera Calibration
112(1)
5.3.2.2 Image Acquisition
112(3)
5.3.2.3 Image Preprocessing
115(2)
5.3.2.4 Extracting and Matching the Reference Points
117(1)
5.3.2.5 Refining the Pairs of Reference Points
118(2)
5.3.2.6 Three-Dimensional Surface Estimation and Image Wrapping
120(1)
5.3.2.7 Texture Enhancement
120(1)
5.3.3 Feature Extraction and Matching Based on Three-Dimensional Templates
120(2)
5.3.4 Unwrapping Three-Dimensional Models
122(1)
5.3.5 Quality Assessment of Touch-Equivalent Fingerprint Images Obtained from Three-Dimensional Models
123(3)
5.3.5.1 Image Segmentation
125(1)
5.3.5.2 Feature Extraction
125(1)
5.3.5.3 Quality Estimation
126(1)
5.4 Computation of Synthetic Touchless Fingerprint Samples
126(8)
5.4.1 Computation of the Silhouette
127(2)
5.4.1.1 Computation of Average Silhouettes from Real Images
127(1)
5.4.1.2 Simulation of the Finger Silhouette
128(1)
5.4.2 Computation of the Three-Dimensional Finger Shape
129(1)
5.4.3 Computation of the Three-Dimensional Ridge Pattern
129(4)
5.4.3.1 Ridge-Pattern Analysis
129(2)
5.4.3.2 Noise Injection
131(1)
5.4.3.3 Computation of the Three-Dimensional Ridges
131(1)
5.4.3.4 Simulation of Camera Focus
131(1)
5.4.3.5 Superimposition of the Ridges
132(1)
5.4.4 Simulation of the Skin Color
133(1)
5.4.5 Simulation of the Illumination Conditions
133(1)
5.4.6 Simulation of a Multiple-View Acquisition
133(1)
5.5 Summary
134(3)
6 Experimental Results 137(58)
6.1 Methods Based on Single Touchless Images
138(16)
6.1.1 Quality Estimation of Touchless Fingerprint Images
138(6)
6.1.1.1 Creation of the Training and Test Datasets
138(2)
6.1.1.2 Application of Proposed Method QB
140(1)
6.1.1.3 Application of Proposed Method QA
140(2)
6.1.1.4 Final Results and Discussion
142(2)
6.1.2 Analysis of Level 1 Features in Touchless Fingerprint Images
144(3)
6.1.2.1 Creation of the Training and Test Datasets
144(1)
6.1.2.2 Computational Intelligence Techniques and Obtained Results
145(2)
6.1.3 Reduction of Perspective and Rotation Effects
147(7)
6.1.3.1 Creation of the Evaluation Datasets and Tuning of the Parameters
147(1)
6.1.3.2 Simulation of Finger Rotation
148(1)
6.1.3.3 Neural Estimation of the Roll-Angle Difference
149(2)
6.1.3.4 Effects of the Proposed Approach on the Performances of the Biometric System
151(3)
6.2 Methods Based on Three-Dimensional Models
154(15)
6.2.1 Three-Dimensional Reconstruction of the Minutiae Points
154(2)
6.2.1.1 Creation of the Training and Testing Datasets
154(1)
6.2.1.2 Classification of Minutiae Pairs
155(1)
6.2.1.3 Computation of Three-Dimensional Minutia
156(1)
6.2.2 Three-Dimensional Reconstruction of the Finger Surface
156(7)
6.2.2.1 The Used Datasets
159(1)
6.2.2.2 Parameters of the Proposed Methods
160(1)
6.2.2.3 Accuracy of the Three-Dimensional Reconstruction Methods
160(3)
6.2.3 Quality Assessment of Unwrapped Fingerprint Images
163(1)
6.2.4 Classification Results
164(2)
6.2.5 Comparison with Literature Methods
166(3)
6.3 Comparison of Biometric Recognition Methods
169(20)
6.3.1 The Used Datasets
170(1)
6.3.2 Parameters Used by Touchless Techniques
171(1)
6.3.3 Accuracy
171(6)
6.3.3.1 Results of the Approach Based on Two-Dimensional Samples
171(2)
6.3.3.2 Results of the Approach Based on Three-Dimensional Samples
173(3)
6.3.3.3 Comparison of Different Technologies
176(1)
6.3.4 Speed
177(2)
6.3.5 Cost
179(1)
6.3.6 Scalability
179(1)
6.3.7 Interoperability
180(1)
6.3.8 Usability
181(4)
6.3.9 Social Acceptance
185(3)
6.3.10 Security
188(1)
6.3.11 Privacy
188(1)
6.3.12 Final Results
188(1)
6.4 Computation of Synthetic Three-Dimensional Models
189(4)
6.5 Summary
193(2)
7 Conclusions and Future Work 195(4)
7.1 Conclusions
195(1)
7.2 Future Work
196(3)
References 199(20)
Index 219
Ruggero Donida Labati, PhD, is a postdoctoral research assistant in computer science at the Università degli Studi di Milano, Italy. He was also a visiting researcher at Michigan State University. His main research interests are biometric systems, biometric encryption and privacy-compliant biometric templates, signal and image processing, computational intelligence algorithms, and industrial applications. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and is a secretariat of the IEEE Italy Section Computational Intelligence Society Chapter. He has over 30 publications in international journals, proceedings of international conferences, and book chapters.

Vincenzo Piuri, PhD, is professor of computer engineering at the Università degli Studi di Milano, Italy, where he has also been director of the Department of Information Technology. His main research interests are biometrics, pattern analysis and recognition, signal and image processing, theory and industrial applications of neural networks, machine learning, intelligent measurement systems, industrial applications, fault tolerance, digital processing architectures, embedded systems, and arithmetic architectures. He is a Fellow of IEEE, the IEEE Vice President for Technical Activities, and IEEE Director. He is editor-in-chief of the IEEE Systems Journal and has been general chair or program chair for over 50 international conferences and workshops.

Fabio Scotti, PhD, is associate professor of computer science at the Università degli Studi di Milano, Italy. His research interests include biometric systems, biometric encryption and privacy-compliant biometric templates, multimodal biometric systems, signal and image processing, computational intelligence algorithms, industrial applications, and high-level system design. He has over 90 publications in international journals, proceedings of international conferences, and book chapters and has been an editor for several books. He is a senior member of IEEE and has chaired or co-chaired a wide variety of conferences, programs, and workshops.
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