(2010) fingerprint force paper
DESCRIPTION
This powerpoint is a copy of the presentation given at the IEEE Carnahan conference in San Jose, 7th October, 2010TRANSCRIPT
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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
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Agenda
Motivation – why are we doing this?
Data Collection
Results
Questions and Further Research
Comments / Questions
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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
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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
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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)
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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
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Methodology - Timing
Throughput is important in an operational setting
What is the impact of force on timing
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Methodology – Comfort Level
Likert scale
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Methodology
Force CaptureSurvey
(disengagement from device)
Force Capture
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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
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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
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Results
Sample description
Force banding
Performance
Throughput
Comfort levels
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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
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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
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Results – Force Distribution
Samples
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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
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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
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Statistical Analysis – Hypothesis #1
Critical value of alpha= 0.05 was chosen
P value was less than 0.05
Power is above 99%
Reject the null
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Statistical Analysis – Hypothesis #2
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)
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Results – Tukey’s HSD Test
Auto-capture image quality scores were similar to 1.5 and 2.5 N
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Results – Detection Error Tradeoff (DET)
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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
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Results – Biometric Subsystem Processing Time
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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
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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.
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Any Questions?
Follow the discussion on the research blog after the conference
www.bspalabs.org/
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Authors and Primary Contact Information
Authors
Benny Senjaya Graduate Researcher at BSPA Lab
Stephen Elliott, Ph.D. BSPA Lab Director & Associate Professor
Shimon Modi, Ph.D. Visiting Scientist at C-DAC Mumbai
Tae Bong Lee, Ph.D. Professor at Kyungwon College
Contact Information
Stephen Elliott, Ph.D.
Associate Professor
Director of BSPA Labs