touchless and less-constrained 3d fingerprint recognition

Touchless and Less-Constrained3D Fingerprint Recognition

Angelo Genovese

Università degli Studi di MilanoDepartment of Computer Science

via Bramante 65, I-26013 Crema (CR), Italyangelo.genovese@unimi.it

December 21, 2015

© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Outline

• Biometricso Traditional biometric systemso Unconstrained and less-constrained biometrics

• Fingerprint biometrics• Touchless fingerprint biometrics

o Touchless fingerprint imageo Possible applications of touchless fingerprint biometricso State of the art of touchless 3D fingerprint methods

• Proposed touchless 3D fingerprint recognitiono Methodo Experiments

• Computation of synthetic 3D fingerprint samples• Touchless 3D reconstruction of ancient fingerprints• Conclusions

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© 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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© 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Unconstrained andless-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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© 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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© 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 fingerprintbiometrics: a survey on 2D and 3D technologies", in Journal of InternetTechnology, May, 2014.

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Possible applications oftouchless fingerprint biometrics

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Touchless 3D fingerprint:state of the art

• 3D reconstruction:o Mosaickingo Structured lighto Multiple viewso 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Touchless fingerprint:some existing systems (1/4)

Mosaicking

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Touchless fingerprint:some existing systems (2/4)

Structured light

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Touchless fingerprint:some existing systems (3/4)

Multiple views

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Touchless fingerprint:some existing systems (4/4)

Absorbed light

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© 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 unconstrainedfingerprint recognition: a fully-touchless 3-D system based on two views on themove", in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015.

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© 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Contactless acquisition (1/4)

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Contactless acquisition (2/4)

Camera A Camera B

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Contactless acquisition (3/4)

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Contactless acquisition (4/4)

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Preprocessing

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Segmentation

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© 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Extraction and matching of the reference points (1/2)

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Extraction and matching of the reference points (2/2)

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© 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

3D surface computationand image wrapping

1. Triangulation

2. Linear interpolation

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Computation oftouch-equivalent images

• EnhancementoBackground subtraction

oNon-linear equalization (logarithm)

oButterworth low-pass filter

• Two-dimensional mapping

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Two-dimensional mapping (1/2)

• Enrollment:oCompensate for rotations

oComputation of 𝑁𝑅 rotations

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Two-dimensional mapping (2/2)

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Template computation

• Neurotechnology VeriFingeroCommercially available

oDesigned for touch-based images

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Matching

• Database entryo𝑁𝑅 templates 𝑇𝑒oOne for each rotation

• Live sampleo1 template 𝑇𝑓

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Experimental results (1/2)

• Scenario evaluationo Touch-based and touchless acquisitions

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© 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Datasets description

• Touchless - one sessiono 2368 sampleso 10 fingers, 30 volunteers, 8 acquisitions per finger

• Touchless - two sessionso 2368 sampleso 10 fingers, 15 volunteers, 16 acquisitions per finger

8 acquisitions one year, 8 acquisition subsequent year

• Touchless - misplaced fingerso 1200 sampleso 2 fingers (index), 30 volunteers, 20 acquisitions per finger

• Touch-basedo One sessiono Two sessions

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Accuracy of 3D reconstruction

• Average error: 0.03m

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Recognition performance (1/2)

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Recognition performance (2/2)

• Comparable to touch-based systemsoOne session

o Two-session

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Robustness to finger misplacements

• Genuine and impostor match scores remainwell separated

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© 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Interoperability

• Accuracy level obtained by matching imagescaptured by different devicesoMatching touchless with touch-based images

2 803 712 identity comparisons

EER = 2.00% with 𝑁𝑅 = 25

Less than EERs obtained in the literature with similar experiments

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© 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Computation of synthetictouchless fingerprint samples (1/2)

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R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Accurate 3D fingerprintvirtual environment for biometric technology evaluations and experimentdesign", Proc. of the 2013 IEEE International Conference on ComputationalIntelligence and Virtual Environments for Measurement Systems andApplications (CIVEMSA 2013), Milan, Italy, July 15-17, 2013, pp. 43-48.

© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Computation of synthetictouchless fingerprint samples (2/2)

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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition

Touchless 3D reconstructionof ancient fingerprints

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R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Two-view contactlessfingerprint acquisition systems: a case study for clay artworks", Proc. of the2012 IEEE Workshop on Biometric Measurements and Systems for Security andMedical Applications (BioMS 2012), Salerno, Italy, September 14, 2012, pp. 1-8.

© 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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© 2015 Angelo Genovese – Touchless and less-constrained 3D fingerprint recognition 46

Publications (1/2)

• Research booko 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 Journalso 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, Manand Cybernetics: Systems, 2015.

o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Touchless fingerprint biometrics: a survey on2D and 3D technologies", in Journal of Internet Technology, vol. 15, no. 3, May 2014, pp. 325-332.

• Refereed Papers in Proceedings of InternationalConferences and Workshops

o R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, Accurate 3D fingerprint virtual environment forbiometric technology evaluations and experiment design", in Proc. of the 2013 IEEE InternationalConference on Computational Intelligence and Virtual Environments for Measurement Systems andApplications (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 neuralapproach for perspective and rotation effects reduction", in Proc. of the 2013 IEEE Symposium onComputational Intelligence in Biometrics and Identity Management (CIBIM 2013), Singapore, April16-19, 2013, pp. 22-30.

© 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 acquisitionsystems: a case study for clay artworks", in Proc. of the 2012 IEEE Workshop on BiometricMeasurements 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 syntheticfingerprints", 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 JointConference 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 singletwo-view structured light acquisition", in Proc. of the 2011 IEEE Workshop on BiometricMeasurements 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 incontact and contactless fingerprint images by using computational intelligence techniques", in Proc.of the IEEE International Conference on Computational Intelligence for Measurement Systems andApplications (CIMSA 2010), Taranto, Italy, September 6-8, 2010, pp. 18-23.