Iris and periocular biometric recognition /

The book reviews developments in the following fields: periocular biometrics; robust iris image segmentation; iris image quality metrics; iris biometric indexing; biometric recognition; best periocular region identification; presentation attack resistant robust biometric ocular system; soft biometri...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Rathgeb, Christian (Editor ), Busch, Christoph (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, UK : The Institution of Engineering and Technology, 2017.
Colección:IET security series ; 5.
IET book series on advances in biometrics.
Temas:
Biometric identification.
Identification biométrique.
COMPUTERS > General.
age issues.
authorisation.
data privacy.
digital forensics.
embedded systems.
gender issues.
image classification.
image matching.
image segmentation.
infrared imaging.
iris recognition.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: 1. Fundamentals in iris recognition
  • 1.1. Introduction / Christoph Busch / Christian Rathgeb
  • 1.2. Iris recognition processing chain / Christoph Busch / Christian Rathgeb
  • 1.2.1. Image acquisition / Christoph Busch / Christian Rathgeb
  • 1.2.2. Pre-processing / Christoph Busch / Christian Rathgeb
  • 1.2.3. Feature extraction / Christoph Busch / Christian Rathgeb
  • 1.2.4. Comparison / Christoph Busch / Christian Rathgeb
  • 1.3. Status quo in research and industry / Christoph Busch / Christian Rathgeb
  • 1.3.1. Databases, competitions and software / Christoph Busch / Christian Rathgeb
  • 1.3.2. Standardization and deployments / Christoph Busch / Christian Rathgeb
  • 1.4. Challenges in iris recognition / Christoph Busch / Christian Rathgeb
  • 1.4.1. Visible wavelength and unconstrained conditions / Christoph Busch / Christian Rathgeb
  • 1.4.2. Security and privacy protection / Christoph Busch / Christian Rathgeb
  • 1.4.3. Cross-sensor and cross-spectral recognition / Christoph Busch / Christian Rathgeb
  • 1.4.4. Soft biometrics and template ageing / Christoph Busch / Christian Rathgeb
  • 1.4.5. Large-scale identification and iris biometric fusion / Christoph Busch / Christian Rathgeb
  • 1.5. Summary / Christoph Busch / Christian Rathgeb
  • References / Christoph Busch / Christian Rathgeb
  • 2. overview of periocular biometrics / Christoph Busch / Christian Rathgeb
  • 2.1. Acquisition of periocular images / Josef Bigun / Fernando Alonso-Fernandez
  • 2.2. Detection of the periocular region / Josef Bigun / Fernando Alonso-Fernandez
  • 2.3. Feature encoding and comparison / Josef Bigun / Fernando Alonso-Fernandez
  • 2.4. Human performance evaluation / Josef Bigun / Fernando Alonso-Fernandez
  • 2.5. Comparison of periocular with other modalities / Josef Bigun / Fernando Alonso-Fernandez
  • 2.6. Other tasks using features from the periocular region / Josef Bigun / Fernando Alonso-Fernandez
  • 2.7. Summary and future trends in periocular research / Josef Bigun / Fernando Alonso-Fernandez
  • Acknowledgments / Josef Bigun / Fernando Alonso-Fernandez
  • References / Josef Bigun / Fernando Alonso-Fernandez
  • 3. Robust iris image segmentation / Josef Bigun / Fernando Alonso-Fernandez
  • 3.1. Introduction / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.1.1. Segmentation accuracy / Peter Wild / Heinz Hofbauer / James Ferryman / Andreas Uhl
  • 3.1.2. Iris segmentation quality / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.2. Advances in iris segmentation / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.2.1. From circular models to active contours / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.2.2. Near infrared vs. visible range segmentation / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.2.3. Learning-based techniques / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.2.4. Segmentation fusion / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.3. Experiments / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.3.1. Individual NIR vs. VIS performance / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.3.2. Impact of tuning / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.3.3. Combinations of segmentation performance / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 3.4. Conclusion and future work / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • References / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 4. Iris image quality metrics with veto power and nonlinear importance tailoring / Peter Wild / Andreas Uhl / James Ferryman / Heinz Hofbauer
  • 4.1. Introduction / Cathryn Downing / John Daugman
  • 4.1.1. Related work / Cathryn Downing / John Daugman
  • 4.2. Normalisation of individual quality measures / Cathryn Downing / John Daugman
  • 4.3. Effectiveness of multiplying quality factors / Cathryn Downing / John Daugman
  • 4.4. Importance tailoring / Cathryn Downing / John Daugman
  • 4.5. Error reject curves / Cathryn Downing / John Daugman
  • 4.6. Discipline in punishment / Cathryn Downing / John Daugman
  • 4.7. Predictive value of the quality pseudonorm / Cathryn Downing / John Daugman
  • 4.8. Testing and adoption into an ISO/IEC quality standard / Cathryn Downing / John Daugman
  • 4.9. possible criticism of the method / Cathryn Downing / John Daugman
  • Acknowledgement / Cathryn Downing / John Daugman
  • References / Cathryn Downing / John Daugman
  • 5. Iris biometric indexing / Cathryn Downing / John Daugman
  • 5.1. Introduction / Jodo C. Neves / Hugo Proenca
  • 5.2. State of the art / Jodo C. Neves / Hugo Proenca
  • 5.3. Indexing/retrieving poorly separated data / Jodo C. Neves / Hugo Proenca
  • 5.3.1. Indexing / Jodo C. Neves / Hugo Proenca
  • 5.3.2. Retrieval / Jodo C. Neves / Hugo Proenca
  • 5.3.3. Time complexity / Jodo C. Neves / Hugo Proenca
  • 5.4. Performance comparison / Jodo C. Neves / Hugo Proenca
  • 5.4.1. Synthetic IrisCodes / Jodo C. Neves / Hugo Proenca
  • 5.4.2. Well separated near infra-red data / Jodo C. Neves / Hugo Proenca
  • 5.4.3. Poorly separated visible wavelength data / Jodo C. Neves / Hugo Proenca
  • 5.5. Conclusions / Jodo C. Neves / Hugo Proenca
  • Acknowledgment / Jodo C. Neves / Hugo Proenca
  • References / Jodo C. Neves / Hugo Proenca
  • 6. Identifying the best periocular region for biometric recognition / Jodo C. Neves / Hugo Proenca
  • 6.1. Introduction / B. VK. Vijaya Kumar / Jonathon M. Smereka
  • 6.2. Experimental setup / B. VK. Vijaya Kumar / Jonathon M. Smereka
  • 6.3. Results / B. VK. Vijaya Kumar / Jonathon M. Smereka
  • 6.4. Summary / B. VK. Vijaya Kumar / Jonathon M. Smereka
  • References / B. VK. Vijaya Kumar / Jonathon M. Smereka
  • 7. Light field cameras for presentation attack resistant robust biometric ocular system / B. VK. Vijaya Kumar / Jonathon M. Smereka
  • 7.1. Introduction / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.2. Light-field imaging / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.3. Scheme for iris and periocular verification / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.3.1. Iris verification / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.3.2. Periocular verification / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.3.3. Combining iris and periocular characteristics / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.3.4. Database, experiments and results / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.4. Presentation attack resistant ocular biometrics in visible spectrum / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.4.1. PAD algorithm employing the depth images from LFC / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.4.2. Visible spectrum iris recognition system / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.4.3. Presentation attack database / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.4.4. Performance evaluation protocol / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.4.5. Experiments and results / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 7.5. Conclusion / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • References / Raghavendra Ramachandra / Christoph Busch / Kiran B. Raja
  • 8. Gender classification from near infrared iris images / Raghavendra Ramachandra / Christoph Busch / Kiran B.
  • Raja
  • 8.1. Introduction / Juan Tapia
  • 8.2. Anatomy structure of the eye / Juan Tapia
  • 8.3. Feature extraction / Juan Tapia
  • 8.4. State of the art / Juan Tapia
  • 8.5. Databases / Juan Tapia
  • 8.5.1. BioSecure multimodal database / Juan Tapia
  • 8.5.2. Gender from iris dataset (ND-GFI) / Juan Tapia
  • 8.6. Feature selection / Juan Tapia
  • 8.7. Research trends and challenges / Juan Tapia
  • 8.7.1. Segmentation / Juan Tapia
  • 8.7.2. Accuracy / Juan Tapia
  • 8.7.3. Fragile bits / Juan Tapia
  • 8.7.4. Sensors / Juan Tapia
  • 8.7.5. Makeup / Juan Tapia
  • 8.8. Concluding remarks / Juan Tapia
  • Acknowledgments / Juan Tapia
  • References / Juan Tapia
  • 9. Periocular-based soft biometric classification / Juan Tapia
  • 9.1. Introduction / Damon L. Woodard / Kalaivani Sundararajan / Nicole Tobias / Jamie Lyle
  • 9.2. Approach / Damon L. Woodard / Jamie Lyle / Nicole Tobias / Kalaivani Sundararajan
  • 9.2.1. Data / Nicole Tobias / Kalaivani Sundararajan / Damon L. Woodard / Jamie Lyle
  • 9.2.2. Preprocessing / Damon L. Woodard / Jamie Lyle / Nicole Tobias / Kalaivani Sundararajan
  • 9.2.3. Feature representations / Damon L. Woodard / Kalaivani Sundararajan / Nicole Tobias / Jamie Lyle
  • 9.2.4. Classification / Damon L. Woodard / Kalaivani Sundararajan / Jamie Lyle / Nicole Tobias
  • 9.3. Experiment results / Damon L. Woodard / Nicole Tobias / Kalaivani Sundararajan / Jamie Lyle
  • 9.3.1. Experiment protocol / Damon L. Woodard / Kalaivani Sundararajan / Jamie Lyle / Nicole Tobias
  • 9.3.2. Gender classification / Damon L. Woodard / Kalaivani Sundararajan / Nicole Tobias / Jamie Lyle
  • 9.3.3. Ethnicity classification / Damon L. Woodard / Kalaivani Sundararajan / Jamie Lyle / Nicole Tobias.
  • Note continued: 9.3.4. Gender and ethnicity classification / Nicole Tobias / Kalaivani Sundararajan / Damon L. Woodard / Jamie Lyle
  • 9.4. Summary / Nicole Tobias / Jamie Lyle / Damon L. Woodard / Kalaivani Sundararajan
  • References / Damon L. Woodard / Kalaivani Sundararajan / Nicole Tobias / Jamie Lyle
  • 10. Age predictive biometrics: predicting age from iris characteristics / Damon L.
  • Woodard / Kalaivani Sundararajan / Nicole Tobias / Jamie Lyle
  • 10.1. Introduction / Marjory Da Costa-Abreu / Meryem Erbilek / Michael Fairhurst
  • 10.2. Background discussion and related studies / Marjory Da Costa-Abreu / Michael Fairhurst / Meryem Erbilek
  • 10.3. Predicting age in diverse scenarios / Michael Fairhurst / Marjory Da Costa-Abreu / Meryem Erbilek
  • 10.4. Experimental infrastructure for iris-based predictive biometrics / Marjory Da Costa-Abreu / Meryem Erbilek / Michael Fairhurst
  • 10.5. Experimental analysis of individual classification systems for iris-based predictive biometrics / Marjory Da Costa-Abreu / Michael Fairhurst / Meryem Erbilek
  • 10.6. Experimental analysis of iris-based multiclassifier systems / Marjory Da Costa-Abreu / Michael Fairhurst / Meryem Erbilek
  • 10.7. Experimental analysis of multiagent intelligent systems for iris-based predictive biometrics / Marjory Da Costa-Abreu / Michael Fairhurst / Meryem Erbilek
  • 10.8. Discussion / Marjory Da Costa-Abreu / Michael Fairhurst / Meryem Erbilek
  • 10.9. Experimental analysis of multimodal iris-based predictive systems / Michael Fairhurst / Marjory Da Costa-Abreu / Meryem Erbilek
  • 10.10. Final remarks / Marjory Da Costa-Abreu / Michael Fairhurst / Meryem Erbilek
  • References / Marjory Da Costa-Abreu / Meryem Erbilek / Michael Fairhurst
  • 11. Presentation attack detection in iris recognition / Marjory Da Costa-Abreu / Meryem Erbilek / Michael Fairhurst
  • 11.1. Introduction / Marta Gomez-Barrero / Javier Galbally
  • 11.2. Presentation attacks in iris recognition / Marta Gomez-Barrero / Javier Galbally
  • 11.3. Presentation attack detection in iris recognition / Marta Gomez-Barrero / Javier Galbally
  • 11.3.1. Sensor-level approaches / Marta Gomez-Barrero / Javier Galbally
  • 11.3.2. Feature-level approaches / Marta Gomez-Barrero / Javier Galbally
  • 11.4. Evaluation of presentation attack detection methods / Marta Gomez-Barrero / Javier Galbally
  • 11.5. Conclusion / Marta Gomez-Barrero / Javier Galbally
  • References / Marta Gomez-Barrero / Javier Galbally
  • 12. Contact lens detection in iris images / Marta Gomez-Barrero / Javier Galbally
  • 12.1. Introduction / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.2. Literature review on contact lens detection / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.2.1. Textured contact lens detection / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.2.2. Classification of contact lens type / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.3. Case study: generalised software-based textured contact lens detection / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.3.1. Pre-processing / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.3.2. Texture description / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.3.3. Experimental analysis / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.4. Discussion / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.4.1. Further work on generalised textured contact lens detection / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.4.2. role of pre-processing in contact lens detection / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 12.4.3. On the evaluation of contact lens detection algorithms / Matti Pietikiiinen / Abdenour Hadid / Jukka Komulainen
  • 12.5. Conclusions / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • Acknowledgement / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • References / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 13. Software attacks on iris recognition systems / Jukka Komulainen / Matti Pietikiiinen / Abdenour Hadid
  • 13.1. Introduction / Marta Gomez-Barrero / Javier Galbally
  • 13.2. Hill-climbing based attacks / Javier Galbally / Marta Gomez-Barrero
  • 13.3. Synthetic iris generation / Javier Galbally / Marta Gomez-Barrero
  • 13.3.1. False acceptance attacks based on fully synthetic iris / Javier Galbally / Marta Gomez-Barrero
  • 13.3.2. Inverse biometrics attacks / Javier Galbally / Marta Gomez-Barrero
  • 13.4. Software attacks on biometric template protection schemes / Javier Galbally / Marta Gomez-Barrero
  • 13.4.1. Stolen key-inversion attacks / Javier Galbally / Marta Gomez-Barrero
  • 13.4.2. Attacks via record multiplicity / Javier Galbally / Marta Gomez-Barrero
  • 13.5. Countermeasures to software attacks / Javier Galbally / Marta Gomez-Barrero
  • 13.6. Conclusions / Javier Galbally / Marta Gomez-Barrero
  • References / Javier Galbally / Marta Gomez-Barrero
  • 14. Iris biometric template protection / Javier Galbally / Marta Gomez-Barrero
  • 14.1. Introduction / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.2. Iris template protection schemes / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.2.1. Iris-biometric cryptosystems / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.2.2. Cancelable iris biometrics / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.3. Implementation of iris template protection schemes / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.3.1. Iris fuzzy vault / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.3.2. Bin-combo for iris-codes / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.4. Experimental evaluations / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.4.1. Performance evaluation / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.4.2. Discussion / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 14.5. Summary and research directions / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • References / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 15. Privacy-preserving distance computation for IrisCodes / Christian Rathgeb / Christoph Busch / Johannes Wagner
  • 15.1. Introduction / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.2. Secure distance computation in the semi-honest model / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.2.1. Oblivious transfer / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.2.2. Yao's garbled circuits / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.2.3. GSHADE in the semi-honest model / Julien Bringer / Herve Chabanne / Constance Morel
  • 15.2.4. Privacy-preserving distance computation for IrisCodes in the semi-honest model / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.3. Secure distance computation in the malicious model / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.3.1. Yao's garbled circuits in the malicious setting / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.3.2. GSHADE in the malicious setting / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.3.3. Privacy-preserving distance computation for IrisCodes in the malicious model / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.4. Application to other iris representations / Julien Bringer / Constance Morel / Herve Chabanne
  • 15.5. Conclusion / Julien Bringer / Constance Morel / Herve Chabanne
  • Acknowledgments / Julien Bringer / Constance Morel / Herve Chabanne
  • References / Julien Bringer / Constance Morel / Herve Chabanne
  • 16. Identifying iris sensors from iris images / Julien Bringer / Constance Morel / Herve Chabanne
  • 16.1. Techniques for sensor identification/dataset classification / Luca Debiasi / Andreas Uhl / Christof Kauba
  • 16.1.1. PRNU-based sensor identification (PSI) / Luca Debiasi / Andreas Uhl / Christof Kauba
  • 16.1.2. Iris texture classification (ITC) / Luca.
  • Debiasi / Andreas Uhl / Christof Kauba
  • 16.2. Datasets / Luca Debiasi / Andreas Uhl / Christof Kauba
  • 16.3. Experimental setup / Luca Debiasi / Andreas Uhl / Christof Kauba
  • 16.4. Experimental results / Luca Debiasi / Andreas Uhl / Christof Kauba
  • 16.5. Practical discussion / Luca Debiasi / Andreas Uhl / Christof Kauba
  • 16.6. Conclusion / Luca Debiasi / Andreas Uhl / Christof Kauba
  • References / Luca Debiasi / Andreas Uhl / Christof Kauba
  • 17. Matching iris images against face images using a joint dictionary-based sparse representation scheme / Christof Kauba / Luca Debiasi / Andreas Uhl
  • 17.1. Introduction / Arun Ross / Raghavender Jillela
  • 17.2. Database / Arun Ross / Raghavender Jillela
  • 17.2.1. Challenges / Arun Ross / Raghavender Jillela
  • 17.3. Outline of experiments / Arun Ross / Raghavender Jillela
  • 17.4. Iris recognition / Arun Ross / Raghavender Jillela
  • 17.4.1. Open source algorithm / Arun Ross / Raghavender Jillela
  • 17.4.2. Commercial algorithm / Arun Ross / Raghavender Jillela
  • 17.5. Ocular recognition / Arun Ross / Raghavender Jillela
  • 17.5.1. Baseline-local binary patterns / Raghavender Jillela / Arun Ross
  • 17.5.2. Normalized gradient correlation / Arun Ross / Raghavender Jillela
  • 17.6. Ocular matching using joint dictionary approach / Arun Ross / Raghavender Jillela.
  • Note continued: 17.6.1. Sparse representation framework / Arun Ross / Raghavender Jillela
  • 17.6.2. Joint dictionary approach / Arun Ross / Raghavender Jillela
  • 17.6.3. Dictionary learning and matching / Arun Ross / Raghavender Jillela
  • 17.7. Computational details / Arun Ross / Raghavender Jillela
  • 17.8. Score-level fusion / Arun Ross / Raghavender Jillela
  • 17.9. Summary / Arun Ross / Raghavender Jillela
  • References / Arun Ross / Raghavender Jillela
  • 18. Iris biometrics for embedded systems / Arun Ross / Raghavender Jillela
  • 18.1. Introduction to embedded systems / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.2. Design strategies for developing embedded systems / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.2.1. General architecture of an embedded system / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.2.2. Architecture alternatives / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.3. Biometric requirements for embedded systems / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.3.1. Functional requirements / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.3.2. Security requirements / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.4. Iris biometrics analysis for embedded systems / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.4.1. Data capture / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.4.2. Preprocessing / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.4.3. Feature extraction / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.4.4. Comparison / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.5. Existing implementations / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.5.1. Custom hardware-FPGA / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.5.2. Software implementations / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 18.6. Conclusions / Raul Sanchez-Reillo / Judith Liu-Jimene
  • References / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 19. Mobile iris recognition / Raul Sanchez-Reillo / Judith Liu-Jimene
  • 19.1. Background / Takashi Shinzaki / Akira Yonenaga
  • 19.2. Mobile iris authentication / Takashi Shinzaki / Akira Yonenaga
  • 19.3. Technologies for the future / Takashi Shinzaki / Akira Yonenaga
  • 19.4. Summary / Takashi Shinzaki / Akira Yonenaga
  • References / Takashi Shinzaki / Akira Yonenaga
  • 20. Future trends in iris recognition: an industry perspective / Takashi Shinzaki / Akira Yonenaga
  • 20.1. Customer requirements / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.1.1. Robustness / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.1.2. Performance / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.1.3. Usability / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.1.4. Choosing a modality / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.2. Development process / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.2.1. Bias and variability / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.2.2. Parameter optimisation / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.2.3. Evaluation and tools / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.3. Future trends and challenges / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.3.1. Sensor-related trends / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.3.2. Presentation attack detection / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.3.3. Deep learning / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.3.4. Standards / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • 20.4. Summary / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • Acknowledgement / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim
  • References / Daniel Hartung / Gunther Mull / Sven Utcke / Ji-Young Lim.

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