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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 1 / 50
Human-Computer Interaction
for Biometrics
Prof. Julian FIERREZ
Universidad Autonoma de Madrid - SPAIN
http://atvs.ii.uam.es/fierrez
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 2 / 50
Funding Acknowledgements
Public Private
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 3 / 50
Outline
1. Biometrics in Access Control Scenarios
2. Authentication in On-Line Applications
3. Biometrics based on HCI
–Handwriting and Signature
–Graphical Passwords
–Swipe Biometrics
–Mouse Dynamics
–Keystroke Dynamics
5. Future Trends & Conclusions
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 4 / 50
Biometrics in Access Control Scenarios
• Biometric access control deployments have concentrated much
efforts and resources in the last 15 years
• Led the market of biometric authentication technologies
• In those scenarios, the user is physically present (in-situ)
• No specific actions from the user are needed (other than placement)
• Once checked, access granted / denied
• If denied, human supervision as an alternative
• Physiological (morphological) modalities are in general better
adapted to these type of tasks
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 5 / 50
Human Activity Patterns
• Human activity patterns are clearly stablished from childhood
• As patterns, they are stable and reproducible, though subject to
variability
• Neuromotor coordination of gestures, movements and speech
• Continuous identity monitoring possible
• User is an active part of the play
• Multilevel strategy: from dynamic trajectories to expressions,
context, habits, stylometry, experiences
• Not fixed patterns but changing and adapting ones
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 6 / 50
Behavioral biometrics, but not neuromotor HDI
Not online HDI (just sensing) for authentication
Human-Computer Interaction for Biometrics
• User is monitored / connected at least for several minutes
• Not only security checking but user convenience scenarios
• Human-device interaction (HDI) is rich in terms of human activity
and behavioral patterns
– Keystroking
– Mouse dynamics
– On-Line Handwritting & Signature
– Graphical Passwords
– Swipe and Gesture Biometrics
– Speech
– Gait
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 7 / 50
Biometric Market by Modality
• Decreasing: Fingerprint, from 48% to 15% (18% excluding AFIS)
• Growing: Iris from 9% to 16% (19%) and Face from 12% to 15% (18%)
• Huge growing: Speech from 6% to 13% (15,5%) and Signature, from 2% to 10% (12%)
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 8 / 50
Active Authentication by DARPA
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 9 / 50
On-line Signature Verification
Feature-based (Global Features)
Distance-based classifiers• Mahalanobis• Euclidean [Nelson et al., 1994]
Statistical/other classifiers• Gaussian Mixture Models
(GMM)• Parzen Windows
Function-based (Local Features)
Time-Sequence matching techniques
• Hidden Markov Models (HMM) [Dolfing et al., 1998]
• Gaussian Mixture Models (GMM) [Richiardi et al., 2005]
• Dynamic Time Warping (DTW) [Sato and Kogure, 1982]
sample index
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Dynamic signature matching
J. Fierrez and J. Ortega-Garcia, "On-line signature verification", A.K. Jain et al. (Eds), Handbook of Biometrics, 2008.
M. Martinez-Diaz and J. Fierrez, "Signature Databases and Evaluation", Stan Z. Li and Anil K. Jain (Eds.), Encyclopedia of Biometrics, Springer, pp. 1367-1375, 2015.
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 10 / 50
Signature Acquisition and Pre-processing
-80 -60 -40 -20 0 20 40 60 80-80
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-20
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20
40
60
80
Key step to greatly reduce sensor interoperability issues due to heterogeneous devices and
writing tools (stylus/finger)
Pre-processing
- Size normalization and centering
- Pressure normalization
- Resampling
M. Martinez-Diaz, J. Fierrez and S. Hangai, "Signature Features", Stan Z. Li and Anil K.
Jain (Eds.), Encyclopedia of Biometrics, Springer, pp. 1375-1382, 2015.
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 11 / 50
Dynamic Time Warping (DTW)Hidden Markov Models (HMM)
Point-to-point correspondenceStatistical modeling of signature regions
M. Martinez-Diaz, J. Fierrez and S. Hangai, "Signature Matching", Stan Z. Li and Anil K. Jain
(Eds.), Encyclopedia of Biometrics, Springer, pp. 1382-1387, 2015.
Similarity Computation
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 12 / 50
• Deep Neural Network- Based System accomplishes:
• Linear Transformations
• Non-Linear Transformations (tangh, sigmoid, ReLU, etc) -> θ
J. Schmidhuber, “Deep Learning in Neural Networks: An Overview”, Neural Networks, vol. 61, pp. 85-117, 2015.
OUTPUT
θ θ
θ θ
θ θ
θ θ
OUTPUTINPUT
Use of Recurrent Neural Networks
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 13 / 50
• Recurrent Neural Networks, well adapted to time sequences (speech, handwritting…)
• Different topologies:
• Long Short-Term Memory (LSTM)
• Gated Recurrent Unit (GRU)
• Also bidirectional versions: BLSTM y BGRU
• Siamese arquitecture
Time Sequence
Memory!
A. Graves, A.R. Mohamed, and G. Hilton, “Towards End-to-End Speech Recognition with Recurrent Neural
Networks”, in Proc. International Conference on Machine Learning, vol. 14, pp. 1764-1772, 2014.
R. Tolosana, R. Vera-Rodriguez, J. Fierrez and J. Ortega-Garcia, "Exploring Recurrent Neural Networks for
On-Line Handwritten Signature Biometrics", IEEE Access, Vol. 6, pp. 5128-5138, February 2018.
Use of Recurrent Neural Networks
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 14 / 50
Traditional Acquisition Scenario (2002-2013)
M. Martinez-Diaz and J. Fierrez, "Signature Databases and Evaluation", Stan Z. Li and Anil K. Jain
(Eds.), Encyclopedia of Biometrics, Springer, pp. 1367-1375, 2015.
sample index
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0 50 100 150 200 250 300 350 4000
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0 50 100 150 200 250 300 350 4000
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0 50 100 150 200 250 300 350 4001000
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0 50 100 150 200 250 300 350 400400
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altitu
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Altitude (0°-90°)
90°
270°
0°
Azimuth (0°-359°)
180°
Altitude (0°-90°)
90°
270°
0°
Azimuth (0°-359°)
180°
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 15 / 50
Our HMM implementation
SVC-04 skilled forgeries
SVC-04 random (zero-effort, casual) impostors
http://www.cs.ust.hk/svc2004/
Benchmarks: SVC 2004
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 16 / 50
BioSec Signature Evaluation Campaign, BSEC’09
• DTW, HMM and Global Systems
• Score normalization
• Fusion of systems
False Acceptance Rate (%)
Fals
e R
ejec
tio
nR
ate
(%)
N. Houmani, et al., "BioSecure signature evaluation campaign(BSEC2009): Evaluating online signature algorithms dependingon the quality of signatures", Pattern Recognition, March 2012.
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 17 / 50
4 training signatures
16 signatures
31 signatures
41 signatures
Random Forg.
97.2 % 99.3 % 99.9 % 99.9 %
Skilled Forg.
88.3 % 93.1 % 95.9 % 99.3 %
• Accuracy (SLT Database):
• State of the art performance
• Template and system configuration update strategies in order to minimize the aging effect
R. Tolosana, R. Vera-Rodriguez, J. Ortega-Garcia and J. Fierrez, "Preprocessing and Feature Selection for Improved
Sensor Interoperability in Online Biometric Signature Verification", IEEE Access, Vol. 3, pp. 478 - 489, May 2015.
BIOTRACE100 Performance (2015)
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 18 / 50
• 5 devices (3 Wacom, 2 Samsung)
• 8 genuine signatures and 6 skilled forgeries per user and device
• Stylus and finger as writing tools (Samsung)
R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, J. Ortega-Garcia, “Benchmarking Desktop and Mobile
Handwriting across COTS Devices: the e-BioSign Biometric Database” PLOS ONE, 2017.
• 70 users, 2 capturing sessions
e-BioSign Database (2016-2017)
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 19 / 50
Challenges on Handheld Devices
Lack of space [Simsons et al., 2011]
Higher client-entropy[Garcia-Salicetti et al., 2008]
Stylus (or finger)
Ergonomics [Blanco-Gonzalo
et al., 2013b]
Standing
position
Lack of in-air trajectories [Sesa-Nogueras et al., 2012]
Sampling
quality
Lack of pressure
and orientation
signals[Muramatsu and
Matsumoto, 2007]
F. Alonso-Fernandez, J. Fierrez and J. Ortega-Garcia, "Quality Measures in Biometric Systems", IEEE Sec. & Privacy, Dec 2012.
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 20 / 50
From Signature to Touch Gestures
• Graphical Password-based User Authentication with Free-form Doodles
M. Martinez-Diaz, J. Fierrez and J. Galbally, "Graphical Password-based User Authentication with Free-Form
Doodles", IEEE Trans. on Human-Machine Systems, August 2016.
M. Martinez-Diaz, J. Fierrez, and J. Galbally. “The DooDB graphical password database: Data analysis and
benchmark results”. IEEE Access, September 2013.
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 21 / 50
Graphical Passwords
• Gesture-based authentication on touch-screens
• Slow typing in touchscreens
• Biometric-rich gestures
• Revocability
Behavioral Biometrics
Physiological Biometrics
Graphical Passwords
M. Martinez-Diaz, J. Fierrez and J. Galbally, "The DooDB Graphical Password Database: Data Analysis and Benchmark Results", IEEE Access, Sept. 2013.
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 22 / 50
Graphical Passwords: Related Works
Pattern Lock [Google]US Patent 20130047252 A1
Multi-touch gestures [Sae-Bae et al., 2012]US Patent 20130219490 A1
Draw a Secret [Jermyn et al., 1999]US Patent 8024775 B2
Pass-Go [Tao et al., 2008]
Picture Gesture Authentication [Microsoft]US Patent 20130047252 A1
1 2
3 4 5
(2,2)
(3,2)
(3,3)
(2,3)
(2,2)
(2,1)
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 23 / 50
Graphical Examples
Doodles Pseudo-signatures
Genuine samples Forgeries Genuine samples Forgeries
M. Martinez-Diaz, J. Fierrez and J. Galbally, "The DooDB Graphical Password Database: Data Analysis and Benchmark Results", IEEE Access, Sept. 2013.
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 24 / 50
Current Work: Swipe Biometrics
Continuous user authentication through touch biometrics:- Security beyond the entry-point
Situation:- Freely interacting with the touchscreen while reading or viewing images
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 25 / 50
Example of strokes captured for two different users
USER A USER B
Session 2, test set
Session 1, training set
High inter-user
variability:
distinctiveness
High intra-user
variability: Difficult to
model the user
Abdul Serwadda, Vir V. Phoha, and Zibo Wang. “Which verifiers work?: A benchmark evaluation of touch-based authentication algorithms”. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pages 1–8, 2013.
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 26 / 50
A. Pozo, J. Fierrez, M. Martinez-Diaz, J. Galbally and A. Morales, "Exploring a Statistical Method for TouchscreenSwipe Biometrics", in Proc. Intl. Carnahan Conference on Security Technology, ICCST 2017, October 2017.
Current Work: Swipe Biometrics
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 27 / 50
Current Work: Mouse Dynamics
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 28 / 50
Mouse Dynamics
• Mouse dynamics analyze
behavior pattern of users for
classification, verification and
identifications.
• Mouse dynamics is a behavioral
biometric characteristic.
Challenges
• High error rates
• High intra-classvariability
Advantages
• No intrusive
• Easy acquisition
• Continuousmonitoring
Intra-class variability (example)
0 100 200 300 400 500 600 7000
100
200
300
400
500
600
700
Distance (pixels)
Dis
tan
ce
(pix
els
)
Sample 1
Sample 2
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 29 / 50
S. Chao, C. Zhongmin, G. Xiaohong and R. Maxion, “Performance evaluation of anomaly-detection algorithms for mouse dynamics,” Computers and Security, vol. 45, pp. 156–171, 2014.
Mouse Dynamics: Features
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 30 / 50
S. Chao, C. Zhongmin, G. Xiaohong and R. Maxion, “Performance evaluation of anomaly-detection algorithms for mouse dynamics,” Computers and Security, vol. 45, pp. 156–171, 2014.
Mouse Dynamics: Performance
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 31 / 50
Keystroke Dynamics
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 32 / 50
Keystroke Dynamics - History
‘Telegraphist style’ during World War II
Keystroke dynamics authentication
emerges with the massive deployment
of computers
Transparent, low-cost solution
Easy to implement in any keypad
device
R. S. Gaines, W. Lisowski, S. J. Press, and N. Shapiro, “Authentication by keystroke timing: Some preliminary results,” tech.
rep., DTIC Document, 1980.
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 33 / 50
Keystroke Dynamics - History
‘Telegraphist style’ during World War II
Keystroke dynamics authentication
emerges with the massive deployment
of computers
Transparent, low-cost solution
Easy to implement in any keypad
device
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 34 / 50
Mobile
Buschek D, De Luca A, Alt F. 2015. Improving accuracy, applicability and usability of keystroke biometrics on mobile touchscreen devices. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI ’15.
Traditional Keyboard
Text-dependent / passwords Text-independent
“In a village of La Mancha, the name of which I have no desire to call to mind, there lived not long since one of those gentlemen that keep a lance in the lance-rack, an old buckler, a lean hack, and a greyhound for coursing…”
Tablet
Keystroke Dynamics: Scenarios
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 35 / 50
From Password-Based to Free-Text Authentication
Substitute password-based authentication + keystroke dynamics
Biometric system as a secondary security level to detect intruders
Free text usually divided into small strings (digraphs and trigraphs)
Match?
DDBB
Keystroke Biometric
DDBB
Text level Biometric level
Accept/Denied
Biometric TemplatesList of passwords
STEP 1 STEP 2
Password
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 36 / 50
From Password-Based to Free-Text Authentication
Substitute password-based authentication + keystroke dynamics
Biometric system as a secondary security level to detect intruders
Free text usually divided into small strings (digraphs and trigraphs)
Keystroke Biometric
DDBB
Matching at biometric feature level
Accept/Denied
Biometric Templates
Free Text
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 37 / 50
M. L. Ali, J. V. Monaco, C. C. Tappert, and M. Qiu. "Keystroke biometric systems for user
authentication." Journal of Signal Processing Systems 86, no. 2-3 (2017): 175-190.
Input sample
Pre-processing
Feature Extraction
Similarity Computation
Score normalization
Decision Threshold
Accepted or rejected
Keystroke Verification Enrolled models
Identity claim
• Sequence alignment through DTW
Keystroke Verification: Sequence Alignment
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 38 / 50
Input sample
Pre-processing
Feature Extraction
Similarity Computation
Score normalization
Decision Threshold
Accepted or rejected
Keystroke Verification Enrolled models
Identity claim
Keystroke Verification: Features
• A. Morales, J. Fierrez and J. Ortega-Garcia , "Towards Predicting Good Users for Biometric Recognition based onKeystroke Dynamics", Proc. of European Conference on Computer Vision Workshops, Springer LNCS-8926, Sept. 2014.
• A. Morales, J. Fierrez, R. Tolosana, J. Ortega-Garcia, J. Galbally, M. Gomez-Barrero, A. Anjos and S. Marcel, "KeystrokeBiometrics Ongoing Competition", IEEE Access, Vol. 4, Nov. 2016.
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 39 / 50
Input sample
Pre-processing
Feature Extraction
Similarity Computation
Score normalization
Decision Threshold
Accepted or rejected
Keystroke Verification Enrolled models
Identity claim
Classifiers based of algorithmic
fusion (GMM, SVM, normalized
Manhattan distance…)
Keystroke Verification: Similarity Computation
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 40 / 50
Keystroke Dynamics: Performance and Benchmark
Performance, strongly user-dependent and scenario-dependent.
Password Authentication (CMU benchmark, 2012) : 8% EER (only
genuine samples during training)
Free-Text, ICB 2015 Competition: 83% Identification Rate (100
characters as query samples, 500 characters as development
samples)
New Benchmark: Keystroke Biometrics Ongoing Competition*
J. V. Monaco, G. Perez, C. C. Tappert, P. Bours, S. Mondal, S. Rajkumar, A. Morales, J. Fierrez and J. Ortega-Garcia,"One-handed Keystroke Biometric Identification Competition", in Proc. IEEE/IAPR Int. Conf. on Biometrics, ICB, May 2015.
*A. Morales, J. Fierrez, R. Tolosana, J. Ortega-Garcia, J. Galbally, M. Gomez-Barrero, A. Anjos and S. Marcel, "KeystrokeBiometrics Ongoing Competition", IEEE Access, Vol. 4, pp. 7746-7746, November 2016.
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 41 / 50
Biometrics Research:
A Look into the Future
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 42 / 50
* J. Fierrez-Aguilar, D. Garcia-Romero, J. Ortega-Garcia and J. Gonzalez-Rodriguez, "Bayesian adaptation for user-dependentmultimodal biometric authentication", Pattern Recognition, August 2005.**J. Fierrez-Aguilar, D. Garcia-Romero, J. Ortega-Garcia and J. Gonzalez-Rodriguez, "Adapted user-dependent multimodal biometricauthentication exploiting general information", Pattern Recognition Letters, December 2005.
Knowledge Base
+ Experiments
+ Experiments’
- Domain adaptation
- Transfer learning
- Inductive transfer
- ...
Bayesian adaptation*
Discriminative adaptation**
The Future of Biometrics based on HCI
Challenge 1: Adapting to New Application Scenarios
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 43 / 50
• P. Aleksic, M. Ghodsi, et al. “Bringing Contextual Information to Google Speech Recognition”, Interspeech, 2015.• F. Alonso-Fernandez, J. Fierrez, et al, “Quality Measures in Biometric Systems”, IEEE Sec. & Privacy, Dec. 2012.• F. Alonso-Fernandez, J. Fierrez, et al., "Quality-Based Conditional Processing in Multi-Biometrics: application to
Sensor Interoperability", IEEE Trans. on Systems, Man and Cybernetics A, Vol. 40, n. 6, pp. 1168-1179, 2010.• J. Fierrez, et al., "Multiple Classifiers in Biometrics. Part 2: Trends and Challenges", Information Fusion, Nov. 2018.
Signal Quality, EnvironmentalData, etc.
Knowledge
Base
The Future of Biometrics based on HCI
Challenge 2: Incorporating Contextual Information
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 44 / 50
44
• Failure to acquireevent
[Simon-Zorita et al. 03]
[Chen et al. 05]
• Q-based fusion
[Bigun et al. 97, 03]
[Fierrez et al. 05, 06]
[Nandakumar et al. 06, 08]
• Q-based featureweighting
[Chen et al. 05]
• Q-basedenhancement
[Hong et al. 98]
• J. Fierrez-Aguilar, J. Ortega-Garcia, J. Gonzalez-Rodriguez and J. Bigun, "Discriminative multimodal biometric
authentication based on quality measures", Pattern Recognition, May 2005.
• J. Fierrez, A. Morales, R. Vera-Rodriguez and D. Camacho, "Multiple Classifiers in Biometrics. Part 2: Trends
and Challenges", Information Fusion, November 2018.
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 45 / 50
• J. Fierrez, J. Ortega-Garcia and J. Gonzalez-Rodriguez, "Target Dependent Score Normalization Techniques and their Application to Signature Verification", IEEE Trans. on Systems, Man and Cybernetics-C, August 2005.
• J. Galbally, M. Martinez-Diaz and J. Fierrez, "Aging in Biometrics: An Experimental Analysis on On-Line Signature", PLOS ONE, July 2013.
• J. Fierrez, A. Morales, R. Vera-Rodriguez and D. Camacho, "Multiple Classifiersin Biometrics. Part 2: Trends and Challenges", Information Fusion, Nov. 2018.
User-specificbehaviour
Knowledge
base
The Future of Biometrics based on HCI
Challenge 3: Adapting to the User (e.g., Aging)
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 46 / 50
Auxiliary Information Obtained during Enrollment: Exploitation of the enrollment data (multiple samples) not only to create the templates/models but also to adjust in a user-dependent way some parameters of the system during verification
e.g., good/bad
users
[Hicklin et al., “The myth of goats: how many people have fingerprints that are hard to match”, NISTIR 7271, 2005].
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 47 / 50
• UD fusion
[Jain et al., ICIP 02]
[Toh et al., TSP 04]
[Snelick et al., PAMI 05]
[Fierrez et al., PR 05]
• UD score normalization
[Fierrez et al., TSMC 05]
[Poh et al., MMUA 06, TASLP 08]
• UD modeling
[Martinez et al., ICFHR 08]
• UD decision (e.g., UD tresholds [Jain et
al., PR 02], failure to enroll events and exception handling)
• UD features
[Fairhurst et al., IPRAI 94]
J. Fierrez, A. Morales, R. Vera-Rodriguez and D. Camacho, "Multiple Classifiers in Biometrics. Part 2: Trends and Challenges", Information Fusion, Nov. 2018.
Exploiting the Zoo (User-Dependent Processing)
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 48 / 50
GLOBAL: Set of training scores from a pool of users (genuine and impostor)LOCAL: Set of training scores from the user at hand (genuine and impostor)
Bayesian and SVM user-dependent fusion algorithms
J. Fierrez, A. Morales, R. Vera-Rodriguez and D. Camacho, "MultipleClassifiers in Biometrics. Part 2: Trendsand Challenges", Information Fusion, Nov. 2018.
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Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 49 / 50
Signer 1
Signer NKnowledge
Base
Big Data
Deep Learning
Anonymized
info
Ruben Tolosana, Ruben Vera, Julian Fierrez, Javier Ortega, “Exploring Recurrent Neural Network for Handwriting Signature Biometrics” IEEE Access, 2018.
The Future of Biometrics based on HCI
Challenge 4: Exploiting Big Data
Julian Fierrez – Seminar at CIMAT, Guanajuato, MEXICO – April 2018 – Slide 50 / 50
Conclusions
• Matureness of technologies (signature, keystroke)
• Major role in on-line and mobile remote applications
• User convenience to drive application development
• Room for substantial industry-applicable research
RevocabilityEasy of use, user acceptanceLess sensor-interoperability issuesEasy to integrate at low-costContinuous ID
User intra-variabilityMulti-sample trainingModel updatingMultilevel strategiesData scarcity