Touchless and less-constrained 3D fingerprint recognition
1 like987 views
The document presents a comprehensive overview of touchless and less-constrained 3D fingerprint recognition, detailing its methodologies and applications in biometrics. It discusses the advantages of touchless systems, such as higher usability and user acceptance compared to traditional methods, along with the technical approaches for 3D fingerprint reconstruction. Experimental results indicate that touchless systems achieve accuracy comparable to contact-based systems while also addressing issues like dust sensitivity and user comfort.
Touchless and less-constrained 3D fingerprint recognition
- 1. Touchless and Less-Constrained 3D Fingerprint Recognition Angelo Genovese Università degli Studi di Milano Department of Computer Science via Bramante 65, I-26013 Crema (CR), Italy angelo.genovese@unimi.it December 21, 2015
- 2. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Outline • Biometrics o Traditional biometric systems o Unconstrained and less-constrained biometrics • Fingerprint biometrics • Touchless fingerprint biometrics o Touchless fingerprint image o Possible applications of touchless fingerprint biometrics o State of the art of touchless 3D fingerprint methods • Proposed touchless 3D fingerprint recognition o Method o Experiments • Computation of synthetic 3D fingerprint samples • Touchless 3D reconstruction of ancient fingerprints • Conclusions 2
- 3. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Biometrics • Traditional recognition methods: o Key, password, smartcard, token • Biometrics: o Behavioral Voice Gait Signature Keystroke o Physiological Fingerprint Iris Hand geometry Palmprint Palmevein Ear ECG DNA 0 0.5 1 1.5 2 2.5 3 3.5 4 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Time (seconds) x Segnale Immagine originale + minuzie NIST (solo per controllare se calcola 3
- 4. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Traditional biometric systems • Low usability and user acceptance: o Complex and highly cooperative acquisition procedures o Can be perceived as privacy invasive 4
- 5. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Unconstrained and less-constrained biometrics • Unconstrained biometrics o Uncooperative subjects o Uncontrolled scenarios • Less-constrained biometrics aim at using samples captured o Contactless o Higher distances o Natural light conditions o On the move o … 5
- 6. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Fingerprint biometrics • The most used biometric trait: o High distinctivity o High permanence • Contact-based sensors: o Low usability and user acceptance o Images with non-linear distortions and low contrast regions o Latent fingerprint on the sensor platen o Sensibility to dust and dirt 6
- 7. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless fingerprint images TouchlessTouch-based R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Touchless fingerprint biometrics: a survey on 2D and 3D technologies", in Journal of Internet Technology, May, 2014. 7
- 8. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Possible applications of touchless fingerprint biometrics 8
- 9. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless 3D fingerprint: state of the art • 3D reconstruction: o Mosaicking o Structured light o Multiple views o Absorbed light • Unwrapping methods: o Parametric models (e.g. cylinder, sphere, set of rings) o Non-parametric models based on minimization functions 9
- 10. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless fingerprint: some existing systems (1/4) Mosaicking 10
- 11. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless fingerprint: some existing systems (2/4) Structured light 11
- 12. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless fingerprint: some existing systems (3/4) Multiple views 12
- 13. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless fingerprint: some existing systems (4/4) Absorbed light 13
- 14. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless 3D fingerprint recognition (1/2) R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Toward unconstrained fingerprint recognition: a fully-touchless 3-D system based on two views on the move", in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015. 14
- 15. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless 3D fingerprint recognition (2/2) • Pros: o Less-constrained o Compensate for rotations o Absence of distortions in the fingerprint images due to different pressures of the finger on the sensor o More robust to dust and dirt o More user acceptance o Possibility to use the recognition methods in mobile devices with standard CCD cameras • Cons: o Longer computational time o Interoperability to be further studied 15
- 16. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Contactless acquisition (1/4) 16
- 17. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Contactless acquisition (2/4) Camera A Camera B 17
- 18. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Contactless acquisition (3/4) 18
- 19. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Contactless acquisition (4/4) 19
- 20. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Preprocessing 20
- 21. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Segmentation 21
- 22. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition 3D reconstruction • Extraction and matching of the reference points. • Refinement of the pairs of corresponding points. • 3-D surface computation and image wrapping. 22
- 23. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Extraction and matching of the reference points (1/2) 23
- 24. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Extraction and matching of the reference points (2/2) 24
- 25. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Refinement of the pairs of corresponding points • Based on Thin Plate Spline 25
- 26. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition 3D surface computation and image wrapping 1. Triangulation 2. Linear interpolation 26
- 27. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Computation of touch-equivalent images • Enhancement o Background subtraction oNon-linear equalization (logarithm) o Butterworth low-pass filter • Two-dimensional mapping 27
- 28. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Two-dimensional mapping (1/2) • Enrollment: o Compensate for rotations o Computation of 𝑁 𝑅 rotations 28
- 29. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Two-dimensional mapping (2/2) 29
- 30. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Template computation • Neurotechnology VeriFinger o Commercially available oDesigned for touch-based images 30
- 31. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Matching • Database entry o 𝑁 𝑅 templates 𝑇𝑒 o One for each rotation • Live sample o 1 template 𝑇𝑓 31
- 32. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Experimental results (1/2) • Scenario evaluation o Touch-based and touchless acquisitions 32
- 33. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Experimental results (2/2) • Datasets description • Accuracy of 3D reconstruction • Recognition performance • Robustness to finger misplacements • User acceptability • Interoperability • Overview of different technologies 33
- 34. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Datasets description • Touchless - one session o 2368 samples o 10 fingers, 30 volunteers, 8 acquisitions per finger • Touchless - two sessions o 2368 samples o 10 fingers, 15 volunteers, 16 acquisitions per finger 8 acquisitions one year, 8 acquisition subsequent year • Touchless - misplaced fingers o 1200 samples o 2 fingers (index), 30 volunteers, 20 acquisitions per finger • Touch-based o One session o Two sessions 34
- 35. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Accuracy of 3D reconstruction • Average error: 0.03m 35
- 36. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Recognition performance (1/2) 36
- 37. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Recognition performance (2/2) • Comparable to touch-based systems o One session o Two-session 37
- 38. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Robustness to finger misplacements • Genuine and impostor match scores remain well separated 38
- 39. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition User acceptability • Survey performed using questionnaires • Results show preference towards contactless recognition 39
- 40. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Interoperability • Accuracy level obtained by matching images captured by different devices o Matching touchless with touch-based images 2 803 712 identity comparisons EER = 2.00% with 𝑁 𝑅 = 25 Less than EERs obtained in the literature with similar experiments 40
- 41. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Overview of the different technologies Aspect Touch-based Touchless Accuracy EER = 0.03% EER = 0.06% Scalability High To be further investigated Interoperability High To be improved Security Latent fingerprints No latent fingerprints Privacy Data protection techniques Data protection techniques Cost 10$ to 5000$ 0$ to 5000$ Usability Medium High User acceptance Medium High Speed Template extraction + matching 3D reconstruction + template extraction + matching 41
- 42. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Computation of synthetic touchless fingerprint samples (1/2) 42 R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Accurate 3D fingerprint virtual environment for biometric technology evaluations and experiment design", Proc. of the 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2013), Milan, Italy, July 15-17, 2013, pp. 43-48.
- 43. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Computation of synthetic touchless fingerprint samples (2/2) 43
- 44. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Touchless 3D reconstruction of ancient fingerprints 44 R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Two-view contactless fingerprint acquisition systems: a case study for clay artworks", Proc. of the 2012 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS 2012), Salerno, Italy, September 14, 2012, pp. 1-8.
- 45. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition Conclusions • Touchless fingerprint recognition: o Systems based on two-dimensional samples can be used in low-cost applications, but the samples present distortions o Systems based on three-dimensional samples can obtain comparable accuracy with respect to traditional systems o Touchless systems are characterized by higher usability, user acceptance, security. o Touchless systems are partially compatible with AFIS 45
- 46. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition 46 Publications (1/2) • Research book o A. Genovese, V. Piuri, and F. Scotti, Touchless Palmprint Recognition Systems", ser. Advances in Information Security, vol. 60, S. Jajodia (ed.), Springer International Publishing, September 2014. • Refereed Papers in International Journals o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Toward unconstrained fingerprint recognition: a fully-touchless 3-D system based on two views on the move", in IEEE Transactions on Systems, Man and Cybernetics: Systems, 2015. o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Touchless fingerprint biometrics: a survey on 2D and 3D technologies", in Journal of Internet Technology, vol. 15, no. 3, May 2014, pp. 325-332. • Refereed Papers in Proceedings of International Conferences and Workshops o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Accurate 3D fingerprint virtual environment for biometric technology evaluations and experiment design", in Proc. of the 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2013), Milan, Italy, July 15-17, 2013, pp. 43-48. o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Contactless fingerprint recognition: a neural approach for perspective and rotation effects reduction", in Proc. of the 2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM 2013), Singapore, April 16-19, 2013, pp. 22-30.
- 47. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition 47 Publications (2/2) o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Two-view contactless fingerprint acquisition systems: a case study for clay artworks", in Proc. of the 2012 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS 2012), Salerno, Italy, September 14, 2012, pp. 1-8. o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Virtual environment for 3-D synthetic fingerprints", in Proc. of the IEEE International Conference on Virtual Environments, Human- Computer Interfaces and Measurement Systems (VECIMS 2012), Tianjin, China, July 2-4, 2012, pp. 48-53. o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Quality measurement of unwrapped three- dimensional fingerprints: a neural networks approach", in Proc. of the 2012 International Joint Conference on Neural Networks (IJCNN 2012), Brisbane, Australia, June 10-15, 2012, pp. 1-8. o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Fast 3-D fingertip reconstruction using a single two-view structured light acquisition", in Proc. of the 2011 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS 2011), Milan, Italy, September 28, 2011, pp. 1-8. o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Measurement of the principal singular point in contact and contactless fingerprint images by using computational intelligence techniques", in Proc. of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2010), Taranto, Italy, September 6-8, 2010, pp. 18-23.