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.

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
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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
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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
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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 …
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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
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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.
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8. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Possible applications of
touchless fingerprint biometrics
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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
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10. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Touchless fingerprint:
some existing systems (1/4)
Mosaicking
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11. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Touchless fingerprint:
some existing systems (2/4)
Structured light
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12. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Touchless fingerprint:
some existing systems (3/4)
Multiple views
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13. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Touchless fingerprint:
some existing systems (4/4)
Absorbed light
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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.
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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
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16. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Contactless acquisition (1/4)
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17. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Contactless acquisition (2/4)
Camera A Camera B
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18. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Contactless acquisition (3/4)
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19. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Contactless acquisition (4/4)
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20. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Preprocessing
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21. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Segmentation
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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.
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23. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Extraction and matching of the
reference points (1/2)
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24. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Extraction and matching of the
reference points (2/2)
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25. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Refinement of the pairs of
corresponding points
• Based on Thin Plate Spline
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26. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
3D surface computation
and image wrapping
1. Triangulation
2. Linear interpolation
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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
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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
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29. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Two-dimensional mapping (2/2)
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30. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Template computation
• Neurotechnology VeriFinger
o Commercially available
oDesigned for touch-based images
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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
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32. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Experimental results (1/2)
• Scenario evaluation
o Touch-based and touchless acquisitions
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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
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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
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35. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Accuracy of 3D reconstruction
• Average error: 0.03m
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36. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Recognition performance (1/2)
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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
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38. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Robustness to finger misplacements
• Genuine and impostor match scores remain
well separated
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39. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
User acceptability
• Survey performed using questionnaires
• Results show preference towards contactless
recognition
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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
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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
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42. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Computation of synthetic
touchless fingerprint samples (1/2)
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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)
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44. © 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition
Touchless 3D reconstruction
of ancient fingerprints
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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
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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.