(2010) Fingerprint Force paper

Biometric Standards, Performance, and Assurance Laboratory |

Purdue University

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Examination of Fingerprint Image Quality and Performance on Force Acquisition vis-à-vis

Auto-captureCarnahan Conference| San Jose, CA| October 7th, 2010

http://www.biotown.purdue.edu/index.html

Biometric Standards, Performance & Assurance Laboratory www.bspalabs.org | www.twitter.com/bspalabs | www.slideshare.net/bspalabs |

http://www.linkedin.com/companies/bspa-labs

Agenda

Motivation – why are we doing this?

Data Collection

Results

Questions and Further Research

Comments / Questions




Why are we doing this?

Force improves the fingerprint image quality and performance

We have done a number of studies in fingerprint force, across 10 print, single print optical and capacitance slap and swipe.

Wanted to examine different force levels and how sensitive force sensor acquisition could be




Four-fold motivation

Validating results from Kukula, et al. (2007)

Difference between auto-capture vs. force-capture

The effect of force-capture on time

User comfort level




Data Collection Setup – Sensor Specifications

Sensor Specifications Digital Persona U.are.U 4000S

Connection to PC USB

Resolution 512 dpi

Platen Size 0.57x0.71”

Operating Temperature – 0°C to +40°C

Operating Humidity 20-80% (non-condensing)

Sensor Specifications

RISC type microcontroller (Atmel – AT90S8535)

Compact Force Sensor (Honeywell – FSS1500)

24-bit high resolution A/D converter (Analog Device AD7710AR)




Methodology – force capture

Examination of force and performance

Auto-capture in Verifinger 5.0

Manipulation of force through the SDK

1.5, 2.5, 3.5, 4.5, 5.5, 6.5, & 7.5 N with tolerance band of

0.5N using force-capture method

Off line analysis using Verifinger 6.0




Methodology - Timing

Throughput is important in an operational setting

What is the impact of force on timing




Methodology – Comfort Level

Likert scale




Methodology

Force CaptureSurvey

(disengagement from device)

Force Capture




Data Collection Procedures

Collected data in accordance with our quality manual (approximates ISO 17025)

Consent forms approved by the IRB

Advertisements were posted around campus

Another data collection activity was ongoing in fingerprinting at the same time

Subjects were seated when they interacted with the fingerprint sensor




Data Collection Procedures

24 fingerprint images were collected per subject

Three images for natural force using auto-capture

method

Three images for each force levels (1.5, 2.5, 3.5, 4.5, 5.5,

6.5, & 7.5 N with tolerance band of 0.5N) using force-

capture method

Survey




Results

Sample description

Force banding

Performance

Throughput

Comfort levels




Results - Subjects

Age Range Frequency

18 – 25 57

26 – 33 8

34 – 41 1

42 – 49 1

50 – 57 1

58 – 65 2

Subjects Distribution

Age Range Distribution




Results – Auto-capture Force Distribution

Force Level (with tolerance ±0.5N)

Frequency

< 1.0 N 98

1.5 N 58

2.5 N 26

3.5 N 19

4.5 N 4

5.5 N 1

6.5 N 0

7.5 N 2

> 8.0 N 2




Results – Force Distribution

Samples




Results – Image Quality Scores (AWARE)

Force Mean Standard Deviation Median

Natural Force 67.92 11.27 70.00

1.5 N 66.86 10.61 69.00

2.5 N 70.13 9.54 72.00

3.5 N 70.96 8.42 72.00

4.5 N 71.84 9.21 73.00

5.5 N 73.06 8.47 74.00

6.5 N 72.96 8.42 75.00

7.5 N 74.62 7.15 76.00

Descriptive Statistics of Image Quality Score




Statistical Analysis – Hypothesis #1

Statistical Test #1 (ANOVA)

Null Hypothesis:

μNF = μ1.5 = μ2.5 = μ3.5 = μ4.5 = μ5.5 = μ6.5 = μ7.5

Alternate Hypothesis:

Not all μ are equal





Critical value of alpha= 0.05 was chosen

P value was less than 0.05

Power is above 99%

Reject the null





Statistical Test #2 (Tukey)

Null Hypothesis:

µi = µj

Alternate Hypothesis:

µi ≠ µj

where i = (NF,1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5)

j = (NF,1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5)




Results – Tukey’s HSD Test

Auto-capture image quality scores were similar to 1.5 and 2.5 N




Results – Detection Error Tradeoff (DET)




Results – False Reject Rate (FRR) at Fixed FAR 0.01%

Force False Reject Rate (FRR)

Natural Force 0.038%

1.5 N 0.0099%

2.5 N 0%

3.5 N 0%

4.5 N 0%

5.5 N 0%

6.5 N 0%

7.5 N 0%

FRR Across Force Levels




Results – Biometric Subsystem Processing Time




Results - Comfort Level

Force Average

1.5 N 4.45

2.5 N 4.58

3.5 N 4.58

4.5 N 4.34

5.5 N 4.17

6.5 N 4.00

7.5 N 3.75

Comfort Level Average




Results - Conclusion

Force impacts both image quality and performance.

By using force-capture acquisition method, the biometric subsystem processing time slightly increases.

Force level 5.5 N is recommended as the optimal force level to be used without sacrificing user’s comfort level.




Any Questions?

Follow the discussion on the research blog after the conference

www.bspalabs.org/




Authors and Primary Contact Information

Authors

Benny Senjaya Graduate Researcher at BSPA Lab

[email protected]

Stephen Elliott, Ph.D. BSPA Lab Director & Associate Professor

[email protected]

Shimon Modi, Ph.D. Visiting Scientist at C-DAC Mumbai

[email protected]

Tae Bong Lee, Ph.D. Professor at Kyungwon College

[email protected]

Contact Information

Stephen Elliott, Ph.D.

Associate Professor

Director of BSPA Labs

[email protected]

mailto:[email protected]





Technology

(2010) Fingerprint Force paper