Narishige Abe, Shigefumi Yamada, Takashi Shinzaki 

FUJITSU LABORATORIES LTD. Evaluation of Minutiae Reproducibility for Seasonal Variation Evaluation of Interoperability between Touch and Sweep Fingerprint Sensors 

Overview 

 Introduction  Fingerprint minutiae rep

 Evaluation 

• Data collection and evaluat• Result 

 Discussion 

• Consideration of the result • How about countermeasure

 Interoperability between 

 Evaluation 

• Data collection and evaluat• Result 

 Discussion 

• What are the causes? • How about countermeasure

 Conclusion 

 Fingerprint verification is widely spread in many fields 

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Introduction 

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http://www.dhs.gov/files/programs/usv.shtm http://pr.fujitsu.com/jp/news/2011/0 6/1.html 

Private 

Public 

Small 

Large 

Lumidigm Venus 

FS-230U 

FS-310U 

Arrows Tab 

LIFEBOOK 

It is critical & important issue to cope with “Seasonal Variation”.

2 

Objective 

 The finger surface cond

depending on factors su

 Climate conditions 

• Temperature • Humidity • Weather 

 Physical conditions 

 “Re-registration” is a ver

 The bigger the scale of the 

• The number of users is als

What influences d

Private 
Related Work: Seasonal Variation 

 Evaluation 

 Fingerprint 

• Statistical inference [Michael et al. 4] • Analysis of effect of 

fingerprint sample quality [Jieun Ryu et al. 5] 

 Iris [P. Tome-Gonzalez et al. 6] 

 Solution 

 Biometric template selection and update: 

a case study in fingerprints [Umut et al. 3] 

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How reproducible are the extracted minutiae? Reproducibility means how accurately the position of minutiae is extracted when comparing extractions performed on the same day and on different days 

[Umut et al. 3] 

4 

Evaluation Method Data Collection 

 Sensors 

 Gardian F, made by CROS PalmSecure 

 Period 

 1st collection: December 2010 

 2nd collection: July 2011 to Oc

 Database information 

Value 

Number of hands measured 2,000 ThMale : Female ratio 1:1 

Age 10’s to 80’s 

 Notes 

 Technical staff support 

• How to input their biometric infto mitigate the affect that the wainput may have on the results 

Evaluation Method Method of Calculating Reproducibility 

 Minutiae extraction 

 NEURO technology’s Verifinger 6.0 

 Compare minutiae 

 Most fingerprint verification engines use features such as: 

• Position of minutiae • Relation among minutiae 

 Spectral minutiae representation based on location (SML) 

IBPC2012 (2012/03/08), Copyright 2012 FUJITSU LIMITED 

Haiyun et al., 2008. 

 Score fusion algorithm 

• One captured image has three fingerprints (index, middle, and ring fingers) • Simple sum rule (Hand = Index + Middle + Ring) 





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iyix

L

yx

LyxL

yxj

M

1

2

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2

(exp( )) 2 exp

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6 

Calculation Result 

Same day 

Different day 

Good 
0.8 

0.6 

Left Hand0.4 0.2 

0 

-0.2 0 0.2 0.4 

-0.2 

Right Hand 

Are There Any Countermeasures? 

 What we learned 

 Fingerprint quality is affected by seasonal variation  The current quality is NOT important, BUT the difference in quality is 

important 

 We have to take care the way to input as well 

 Countermeasures 

 Try another finger or the other hand 

Any Countermeasures? 

Try the Other Hand 

 Measuring one hand is d

 It is a very natural counterm

 The difficulty of measuri

other hand 

 Correlation coefficient of 

same day and on differe

 Image enhancement  Template update  Prediction methods? 

8 IBPC2012 (2012/03/08), Copyright 2012 FUJITSU LIMITED 

Any Countermeasures? Others 

 Image enhancement 

 Image enhancement [1,2,7] 

• Filter base [8,9,10,11,12,13] • Others [14,15] 

 Evaluation [16] 

We have to take care not to over-enhance 

 Template update 

 System updates updated to the latest fingerprint data  System can keep up with changes in the finger surface 

• Cannot keep up with drastic changes • Difficult to collect fingerprints constantly (e.g., at border control) 

 Prediction method 

 What kind of quality is related to seasonal variation? 

• Traditional quality measures represent the quality when captured • It may be impossible to predict the future input’s quality: 

• Next input / Six months later 

 It may be necessary to use time-series quality We do not have any solution so far 

10 IBPC2012 (2012/03/08), Copyright 2012 FUJITSU LIMITED 

Even if a user changes to 

Minutiae Reproducibility for SeaConclusion & Summ

 Result 

 Data from younger people 

people 

 Seasonal variation influenc As the age rises, the reprod

 Countermeasures 

 Try the other hand? 

• Even if a user changes to t

 Image enhancement 

• Enhancement may be effec

 Template update 

• Updating may be effective, 

 Predict method? 

• Future work 
 Fingerprint verification is widely spread in many fields 

Large 

Introduction 

IBPC2012 (2012/03/08), Copyright 2012 FUJITSU LIMITED 

http://www.dhs.gov/files/programs/usv.shtm http://pr.fujitsu.com/jp/news/2011/0 6/1.html 

Private 

Small 

It is critical & important issue to cope with “Seasonal Variation”.

It is useful to integrate sweep sensors in existing systems- Many devices have this type of sensor Public - Low price

12 

Evaluation of between ToucFingerprint Se

 Go from using a console and touch sensor to a mobile device 

and sweep sensor 

 We want to use the existing system with a sweep sensor  Templates in the existing system were created with a touch sensor 

Objective 

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How about interoperability?

Database 

14 

Related Work: Intero

 Evaluation 

 Fingerprint [17, 18, 22]  Signature [21] 

 Solution 

 Segmentation [19]  Feature selection [20]  Calibration [23, 25]  Fusion [24] 

How about interopera
Evaluation Method 

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 Data collection 

 Environment 

• Sensor 

• FS-230U (by FUJITSU) • AES2501 (by AuthenTec) 

 Database information 

Value Note 

Number of fingers measured 6,000 There were 1,500 subjects. Male : Female ratio 1:1 -

Age 10 to 89 -

 Notes 

• Technological staff gave instructions and helped the subjects to input their 

biometric information, in order to mitigate the affect that the way information is 

input may have on the results 

 Extractor & verification engine 

 NEURO technology’s Verifinger 6.0 in order to be able to test again. 

16 

Simulation Result DET Curve 

Cross-verification result is ab

Simulation Result Verification Success Rate by Finger 

 Rate of difficult-to-measure fingers 

 Fingers with a verification success rate of under 50% 

 Rate of fingers that could not be verified 

 Fingers with a verification success rate of 0% when conducting crossverification 

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(Enroll) 

(Verify) 

(Touch) (Touch) 

(Sweep) (Sweep) 

(Touch) 

(Sweep) 

(Sweep) 

(Touch) 

FMR Difficult 0.3% 0.3% 3.0% 2.8% 

0.1% Impossible - - 1.2% 1.4% 

FMR Difficult 0.4% 0.3% 3.6% 3.6% 

0.01% Impossible - - 1.9% 1.8% 

FMR Difficult 0.5% 0.4% 4.2% 4.2% 

0.001% Impossible - - 2.2% 2.1% 

FMR Difficult 0.6% 0.5% 5.0% 5.0% 

0.0001% Impossible - - 2.7% 2.7% 

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What Kind of Differences Are ThDifferences of The W

Touch-Sensor 

Strong Weak 

Finger Pressure 

Too far right or left 

Too high up or low 

down 

Put Motion 

Rotation 

It is more difficult to 
1 Deforming captured image 

 There are many algorithms: 

• Fingerprint deformation [29, 27, 33] • Fingerprint mosaicking [28, 31, 32, 34] • Matching [30] 

2 Stabilizing input motion with sweep sensor 

 Design an appropriate guide and apparatus  Users have to learn the right way to swipe 

Before lecture After lecture 

Are There Any Countermeasures? 

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It is important to consider variation input

Teaching is very effective

20 

Interoperability between Touch Conclusion & Summ

 Evaluation 

 We evaluated the interope FNMR increases 7 - 8 time

verified 

 What are the causes? 

 Difference characteristics o More variation in how peop

 Countermeasures 

 Teaching users how to inp Deforming fingerprints may 

Conclusion & Summary 

 Minutiae reproducibility for seasonal variation 

 Seasonal variation influences data from people of all ages  Countermeasures 

1 Try the other hand 2 Image enhancement or template update (if possible) 3 Predict method (future work) 

 Interoperability between touch and sweep sensors 

 About 1 - 3% of fingers cannot be verified  Countermeasures 

• How much FAR you set • Teaching users how to input is very effective 

22 IBPC2012 (2012/03/08), Copyright 2012 FUJITSU LIMITED 

Reference (1/4)

 Overview 1. Davide Maltoni, Dario Maio, Anil K. J

Recognition”, Springer professional 

2. Nalini Ratha, Ruud Bolle: “Automatic  Template ageing 3. Umut Uludag, Arun Ross, Anil Jain: 

fingerprints”, Pattern Recognition 37

4. Michael E. Schuckers: “Statistical In

6202, 2006. 

5. Jieun Ryu, Jihyeon Jang, Hale Kim: 

Template Ageing”, NIST Biometric Qhttp://biometrics.nist.gov/cs_links/qunt_Sample_Quality_in_Template_Ag

6. P. Tome-Gonzalez, F. Alonso-Ferna

Variability in Iris Recognition”, BTAS 
Reference (2/4) 

 Fingerprint Enhancement 7. Lin Hong, Yifei Wan, and Anil Jain: “Fingerprint Image Enhancement: Algorithm and Performance Evaluation”, IEEE Trans. Vol. 20, No. 8, Aug. 1998. 

8. Jianwei Yang, Lifeng Liu, Tianzi Jiang, Yong Fan: “A modified Gabor filter design method for fingerprint image enhancement”, Pattern Recognition Letters 24(12), 1805-1817, 2003. 

9. Safar Hatami, Reshad Hosseini, Mahmoud Kamarei, Hossein Ahmadi: “Wavelet Based Fingerprint Image Enhancement”, ISCAS, IEEE, Vol. 5, 4610-4613, 2005. 10. Joon Hwan Choi, Seung-Rae Lee, Seong-eun Roh, and Taejeong Kim: PERCEPTION-BASED FINGERPRINT IMAGE ENHANCEMENT”, International Symposium on Signal Processing and its Applications, IEEE, 2007. 11. Huiqing Chen, Gang Dong: “FINGERPRINT IMAGE ENHANCEMENT BY DIFFUSION PROCESS”, International Conference on Image Processing, IEEE, 297-300, 2006. 12. Joachim Weickert: “Coherence-Enhancing Shock Filters”, Proc. DAGM, pp. 1-8, 2003. Marcelo de Almeida Oliveira, Neucimar Jeronimo Leite: “Reconnection of Fingerprint Ridges Based on Morphological Operators and Multiscale Directional Information”, SIBGRAPI ’04, 122-129, 2004. 13. Michael Breuz and Joachim Weickert: “A Shock-Capturing Algorithm for the Differential Equations of Dilation and Erosion”, Journal of Mathematical Imaging and Vision, Vol. 25, 187-201, 2006. 14. Jos B. T. M. Roerdink and Arnold Meijster: “The Watershed Transform: Definitions,Algorithms and Parallelization Strategies”, Fundamenta Informaticae 41, 187-228, 2001. 15. Sharat Chikkerur, Alexander N. Cartwright, Venu Govindaraju: “Fingerprint Enhancement Using STFT Analysis”, Pattern Recognition, Elsevier Science, Vol. 40, 198-211, 2007. 16. Zhifang Wang, Shenghong Chen, Christoph Busch and Xiamu Niu: “Performance Evaluation of Fingerprint Enhancement Algorithms”, 2008 Congress on Image and Signal Processing, 2008. 

24 IBPC2012 (2012/03/08), Copyright 2012 FUJITSU LIMITED 

Reference (3/4) 

 Interoperability Evaluation 17. Davrondzhon Gafurov, Christoph Bus

Authentication Two Finger Types vs. T(Applied Biometrics Track), TaiChung

18. Davrondzhon Gafurov, Patrick Bours, 

Fingerprint Database for In-House (SeInternational Conference on Computat

19. Xinjian Guo, Gongping Yang, Yilong Y

Empirical Study”, IEEE 2009. 

20. Chunxiao Ren, Yilong Yin, Jun Ma, an

Interoperability: A Case Study in FingeInternational Conference on Systems, 

21. Fernando Alonso-Fernandez, Julian Fi

Interoperability and Fusion in Signature LNCS 3781, pp. 180-187, 2005. 

22. Arun Ross and Anil Jain: “Biometric Se

LNCS Vol. 3087, pp. 134-145, Springe

23. Arun Ross and Rohan Nadgir: “A Calib

SPIE Conference on Biometric Techno

24. F. Alonso-Fernandez, R. N. J. Veldhuis

“Sensor Interoperability and Fusion in Ridge-Based Matchers”, IEEE, 2006. 

25. Arun Ross and Rohan Nadgir: “A Thin-

Interoperability”, IEEE Transactions on 2008. 

Reference (4/4) 

 Deformation & Mosaicking 27. Yi Chen, Sarat Dass, Arun Ross, and Anil Jain: “Fingerprint Deformation Models Using Minutiae Locations and Orientation”, Proceedings of the Seventh IEEE Workshop on Applications of Computer Vision, 2005. 

28.Kyoungtaek Choi, Heeseung Choi, Sangyoun Lee, and Jaihie Kim: “Fingerprint Image Mosaicking by Recursive Ridge Mapping”, IEEE Transactions on Systems, Man, And Cybernetics Part B: Cybernetics. Vol. 37, No. 5, October 2007, 2007. 29. Arun Ross, Sarat C. Dass, and Anil K. Jain: “Fingerprint Warping Using Ridge Curve Correspondences”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No.l 1, January 2006, 2006. 30. Hui Chen, Jianping Yin, Xin Shu, Chungeng Hu, Yong Li: “Minutiae Location and Direction Adjustment for Nonlinear Distorted Fingerprint Image Matching”, 2010 2nd International Conference on Computer Engineering and Technology, 2010. 

31. Asem Othman and Arun Ross: “Mixing Fingerprints For Generating Virtual Identities”, Proc. of IEEE International Workshop on Information Forensics and Security, 2011. 

32. Heeseung Choi, Kyoungtaek Choi, and Jaihie Kim: “Mosaicing Touchless and Mirror- Reflected Fingerprint Images”, IEEE Transactions on Information Forensics And Security, Vol. 5, No. 1, March 2010, 2010. 

33. C.X. Ren, Y.L. Yin, J. Ma, and H. Li, “Fingerprint scaling”, International Conference on Intelligent Computing (ICIC 2008), Springer Lecture Notes in Computer Science (LNCS), Vol. 5226, pp. 474-481, Shanghai, China, Sep. 16-18, 2008. 

34. Neil Yager and Adnan Amin: “Evaluation of Fingerprint Orientation Field Registration Algorithms”, in Proc. 17th ICPR, 2004, vol. 4, pp. 641-644, 2004. 

26 IBPC2012 (2012/03/08), Copyright 2012 FUJITSU LIMITED