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(2010) Fingerprint Force paper
 

(2010) Fingerprint Force paper

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This powerpoint is a copy of the presentation given at the IEEE Carnahan conference in San Jose, 7th October, 2010

This powerpoint is a copy of the presentation given at the IEEE Carnahan conference in San Jose, 7th October, 2010

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    (2010) Fingerprint Force paper (2010) Fingerprint Force paper Presentation Transcript

    • Examination of Fingerprint Image Quality and Performance on Force Acquisition vis-à-vis Auto-captureCarnahan Conference| San Jose, CA| October 7th, 2010
      Biometric Standards, Performance, and Assurance Laboratory |
      Purdue University
      www.bspalabs.org
      www.twitter.com/bspalabs
      www.slideshare.net/bspalabs
      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
    • 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
    • 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 Distribution
      Subjects Distribution
    • Results – Auto-capture Force Distribution
    • Results – Force Distribution
      Samples
    • Results – Image Quality Scores (AWARE)
      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
    • 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
    • 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)
    • 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%
      FRR Across Force Levels
    • Results – Biometric Subsystem Processing Time
    • Results - Comfort Level
      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
      bennysenjaya@gmail.com
      Stephen Elliott, Ph.D.
      BSPA Lab Director & Associate Professor
      elliott@purdue.edu
      Shimon Modi, Ph.D.
      Visiting Scientist at C-DAC Mumbai
      shimonmodi@gmail.com
      Tae Bong Lee, Ph.D.
      Professor at Kyungwon College
      tblee@kyungwon.ac.kr
      Contact Information
      Stephen Elliott, Ph.D.
      Associate Professor
      Director of BSPA Labs
      elliott@purdue.edu