Handbook of Biometrics for Forensic Science
This comprehensive handbook addresses the sophisticated forensic threats and challenges that have arisen in the modern digital age, and reviews the new computing solutions that have been proposed to tackle them. These include identity-related scenarios which cannot be solved with traditional approac...
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| Hlavní autoři: | , |
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Cham
Springer Nature
2017
Springer Springer International Publishing AG Springer International Publishing |
| Vydání: | 1 |
| Edice: | Advances in Computer Vision and Pattern Recognition |
| Témata: | |
| ISBN: | 9783319506739, 3319506730, 3319506714, 9783319506715 |
| ISSN: | 2191-6586, 2191-6594 |
| On-line přístup: | Získat plný text |
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- 10.3.1 Scoring Method -- 10.3.2 Direct Method -- 10.4 Performance Evaluation -- 10.4.1 Performance Characteristics and Metrics -- 10.4.1.1 Performance Characteristics-Tippett Plots -- 10.4.1.2 Performance Metrics -- Performance Metric 1-Probabilities of Misleading Evidence (PMEH0 and PMEH1) -- Performance Metric 2-Equal Proportion Probability (EPP) -- Performance Metric 3-Log-Likelihood-Ratio Cost (Cllr) -- 10.4.2 Evaluation of Case-Specific Strength of Evidence -- 10.5 Conclusion -- References -- 11 On Using Soft Biometrics in Forensic Investigation -- Abstract -- 11.1 Introduction -- 11.2 Forensic Case Work as It Is Performed Today -- 11.2.1 Forensic Image Analysis at Present -- 11.2.2 Presentation of Findings in Court -- 11.2.3 Directions of Further Research -- 11.3 A Software Platform to Support Forensic Investigations: BioFoV -- 11.3.1 User Interface -- 11.3.2 Modules -- 11.3.2.1 Camera Calibration -- 11.3.2.2 Event Detection -- 11.3.2.3 Re-Projected Image Plane Measurements -- 11.3.2.4 Feature Extraction-Face Detection Example -- 11.3.3 How to Get BioFoV -- 11.4 Applications of 3D Markerless Motion Capture in Forensic Gait Analysis -- 11.4.1 Accurate 3D Imaging of Human Gait and Bodily Dimensions -- 11.4.2 Using Gait Kinematics and Random Forests for Recognition -- 11.4.3 3D Surveillance and Future Perspectives in Gait Recognition -- 11.5 Extraction of Soft Biometrics from Facial Images -- 11.5.1 Extracting Gender from Face Images -- 11.5.2 Age Classification from Facial Images -- 11.5.3 Ethnicity Classification from Facial Images -- 11.5.4 Experimental Analysis on Extracting Facial Soft Biometrics from Videos -- 11.5.4.1 Static Image-Based Approach -- 11.5.4.2 Spatiotemporal-Based Approach -- 11.5.4.3 Experiments on Gender Recognition -- 11.5.4.4 Experiments on Age Estimation
- Intro -- Preface -- Contents -- 1 Biometric Technologies for Forensic Science and Policing: State of the Art -- Abstract -- 1.1 A Short Historical Introduction and Forensic Context -- 1.2 Recent Developments of Biometric Technologies in Forensic Science -- 1.3 Challenges -- 1.4 Conclusions -- Acknowledgements -- References -- Analysis of Fingerprints and Fingermarks -- 2 Capture and Analysis of Latent Marks -- Abstract -- 2.1 Introduction -- 2.2 Fingerprint Characteristics -- 2.3 Conventional Latent Mark Acquisition Techniques -- 2.4 Contact-Less Latent Mark Acquisition Techniques -- 2.5 Latent Mark Analysis Process -- 2.6 Legal Challenges of Applying New Techniques in the Latent Mark Processing -- 2.7 Summary -- References -- 3 Automated Fingerprint Identification Systems: From Fingerprints to Fingermarks -- Abstract -- 3.1 Introduction -- 3.1.1 History -- 3.1.2 AFIS Functionalities -- 3.1.3 Fingerprint Identification Accuracy -- 3.2 Automated Fingerprint/Mark Technology -- 3.2.1 Fingerprints -- 3.2.2 Fingermarks -- 3.3 Segmentation -- 3.4 Enhancement -- 3.5 Forensic Applications -- 3.5.1 Applications Using fingerprints -- 3.5.1.1 Identity Management Within Criminal Justice Systems -- 3.5.1.2 Forensic Identification of Missing Persons -- 3.5.2 Application Using Fingermarks -- 3.5.2.1 Forensic Intelligence -- 3.5.2.2 Forensic Investigation -- 3.5.2.3 Forensic Evaluation -- 3.5.3 Current Challenges -- 3.5.3.1 Automation and Transparency -- 3.5.3.2 Scalability and Interoperability -- 3.5.3.3 Forensic Fingermark Processes -- 3.6 Conclusion -- References -- 4 Challenges for Fingerprint Recognition-Spoofing, Skin Diseases, and Environmental Effects -- Abstract -- 4.1 Spoofing and Anti-spoofing -- 4.1.1 Perspiration -- 4.1.2 Spectroscopic Characteristics -- 4.1.3 Ultrasonic Technology -- 4.1.4 Physical Characteristics: Temperature
- 4.1.5 Physical Characteristics: Hot and Cold Stimulus -- 4.1.6 Physical Characteristics: Pressure Stimulus -- 4.1.7 Physical Characteristics: Electrical Properties -- 4.1.8 Physical Characteristics: Pulse -- 4.1.9 Physiological Basics of Heart Activity -- 4.1.10 Physical Characteristics: Blood Oxygenation -- 4.1.11 Fingerprint Spoof Preparation -- 4.2 Skin Diseases -- 4.3 Environmental Distortions -- 4.3.1 Phenomena Influencing Fingerprint Acquisition -- 4.3.2 Methods for Generation of Synthetic Fingerprints -- 4.4 Conclusion -- Acknowledgments -- References -- 5 Altered Fingerprint Detection -- 5.1 Introduction -- 5.2 Background of Fingerprint Alterations -- 5.2.1 Obliteration -- 5.2.2 Distortion -- 5.2.3 Imitation -- 5.3 Related Work -- 5.3.1 Orientation Field Analysis -- 5.3.2 Minutiae Distribution Analysis -- 5.4 Recent Algorithms for Fingerprint Alteration Detection -- 5.4.1 Preprocessing -- 5.4.2 Singular Point Density Analysis -- 5.4.3 Minutia Orientation Analysis -- 5.4.4 Orientation Difference Map -- 5.4.5 Orientation Density Map -- 5.5 Evaluation and Results -- 5.6 Conclusion -- References -- Face and Video Analysis -- 6 Face Sketch Recognition via Data-Driven Synthesis -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Sparse Representation Supported Candidate Selection Methods -- 6.3.1 Sparse Feature Selection Based Face Sketch Synthesis -- 6.3.2 Sparse Representation Based Greedy Search for Face Sketch Synthesis -- 6.4 Graphical Representation Based Reconstruction Models -- 6.4.1 Transductive Face Sketch Synthesis -- 6.4.2 Multiple Representation Based Face Sketch Synthesis -- 6.5 Experimental Results -- 6.6 Conclusion -- References -- 7 Recent Developments in Video-Based Face Recognition -- 7.1 Introduction -- 7.2 Sparse Coding-Based Methods -- 7.3 Manifold-Based Methods -- 7.4 Probabilistic Methods -- 7.5 Geometrical Model-Based Methods
- 11.5.4.5 Experiments on Ethnicity Classification (Asian Versus Non-Asian) -- 11.5.4.6 Discussion -- 11.6 Conclusions -- References -- 12 Locating People in Surveillance Video Using Soft Biometric Traits -- 12.1 Introduction -- 12.2 Prior Work -- 12.3 Modelling Traits -- 12.4 Locating People Using a Region-Based Approach -- 12.4.1 Search Query Formulation -- 12.4.2 Searching for a Target -- 12.4.3 Assessing Clothing Type -- 12.5 Searching Using a Channel Representation -- 12.5.1 Generating an Avatar -- 12.5.2 Searching for a Target -- 12.5.3 Compensating for Scale -- 12.6 Database and Evaluation Protocol -- 12.6.1 Data -- 12.6.2 Evaluation Protocol -- 12.7 Results -- 12.7.1 Computational Efficiency and Scalability -- 12.8 Conclusions and Future Work -- References -- 13 Contact-Free Heartbeat Signal for Human Identification and Forensics -- Abstract -- 13.1 Introduction -- 13.2 Measurement of Heartbeat Signal -- 13.2.1 Contact-Based Measurement of Heartbeat Signal -- 13.2.2 Contact-Free Measurement of Heartbeat Signal -- 13.2.2.1 Motion for Contact-Free Extraction of Heartbeat Signal -- 13.2.2.2 Color for Contact-Free Extraction of Heartbeat Signal -- 13.3 Using Heartbeat Signal for Identification Purposes -- 13.3.1 Human Identification Using Contact-Based Heartbeat Signal -- 13.3.2 Human Identification Using Contact-Free Heartbeat Signal -- 13.4 Discussions and Conclusions -- References -- Statistical Analysis of Forensic Biometric Data -- 14 From Biometric Scores to Forensic Likelihood Ratios -- 14.1 Likelihood Ratio Framework for Evidence Evaluation -- 14.1.1 Challenges in LR-Based Evidence Evaluation -- 14.2 Case Assessment and Interpretation Methodology -- 14.3 Evidence Evaluation with Likelihood Ratios -- 14.4 Interpreting Biometric System Scores with Likelihood Ratios -- 14.5 LR Computation Methods from Biometric Scores
- 7.6 Dynamical Model-Based Methods -- 7.7 Conclusion and Future Directions -- References -- 8 Face Recognition Technologies for Evidential Evaluation of Video Traces -- 8.1 Introduction -- 8.2 Automatic Face Recognition -- 8.2.1 Face Detection -- 8.2.2 Feature Extraction -- 8.2.3 Matching -- 8.3 Face Recognition from Videos Traces -- 8.4 Handling Uncontrollable Factors Present in Videos -- 8.4.1 Approaches for Handling Pose Variations -- 8.4.2 Approaches for Handling Occlusion Variations -- 8.4.3 Approaches for Handling Illumination Variations -- 8.4.4 Approaches for Handling Low Image Quality Variations -- 8.5 Future Trends -- 8.5.1 Combining with Other Biometric Traits -- 8.5.2 Contending with the Face Ageing Issue -- 8.5.3 Different Imaging Modalities -- 8.5.4 Other Issues in Forensic Tasks -- 8.6 Summary -- References -- 9 Human Factors in Forensic Face Identification -- Abstract -- 9.1 Introduction -- 9.1.1 The Problem -- 9.2 Characteristics of Human Face Recognition Relevant for Forensics -- 9.2.1 Familiarity -- 9.2.2 Image and Demographic Factors -- 9.2.2.1 Stimulus Factors -- 9.2.2.2 Subject Factors -- 9.2.2.3 Interactive Factors -- 9.3 Are Facial Image Comparison "Experts" More Accurate at Facial Image Comparison Than Untrained People? -- 9.4 Can Computer-Based Face Identification Systems Address Weaknesses of the Forensic Examiner and the Forensic Examination Process? -- 9.4.1 Unfamiliar Face Recognition Tasks for Machines -- 9.4.2 Measuring Human Performance for Comparison with Machines -- 9.4.3 Measuring Human Performance for Comparison with Machines -- 9.5 Discussion and Future Directions -- References -- Human Motion, Speech and Behavioral Analysis -- 10 Biometric Evidence in Forensic Automatic Speaker Recognition -- Abstract -- 10.1 Introduction -- 10.2 Biometric Evidence in FASR -- 10.3 Calculation of Likelihood Ratio (LR)
- 14.5.1 Generating Training Scores