(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
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The human signature provides a natural and publically-accepted legally-admissible method for providing authentication to a process. Automatic biometric signature systems assess both the drawn image and the temporal aspects of signature construction, providing enhanced verification rates over and above conventional outcome assessment. To enable the capture of these constructional data requires the use of specialist ‘tablet’ devices. In this paper we explore the enrolment performance using a range of common signature capture devices and investigate the reasons behind user preference. The results show that writing feedback and familiarity with conventional ‘paper and pen’ donation configurations are the primary motivation for user preference. These results inform the choice of signature device from both technical performance and user acceptance viewpoints.Entropy of Fingerprints
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The BDS model marries the general biometric model with HBSI model to create a logical flow of five phases: the presentation definition phase, sample phase, processing phase, and enrollment or matching phase. By tracking information through this progression such as specific subject presentations made, HBSI error, and FTE/Enrollment score (to name a few), performance within the general biometric model can be examined. The BDS model goes one step further by creating specific durations to report research specific metrics. By creating this model, outcomes that effect a yearly performance metrics can be looked at by examining monthly performance, daily performance, or even specific user presentations and how those subcomponents effect the whole system.
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This course provides an overview of biometric technology as it relates to security, access control, and authentication. It examines basic biometric terminology and various biometric modalities such as fingerprint, face, and iris recognition. Students will learn about biometric data evaluation and interpretation, standards, integration, and challenges. The course is divided into fundamental, modality, integration, and research building blocks to cover topics like identification, matching, fusion, standards, and interoperability.
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This document outlines the structure and goals of a research study on the stability of iris recognition match scores over time. It introduces the problem statement around the lack of quantification of match score stability, and previews the research question, significance, purpose and scope, assumptions, limitations, and delimitations that will be discussed in the following chapters which focus on the literature review, methodology, results, and conclusions of the study.
(2010) Fingerprint recognition performance evaluation for mobile ID applications
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According to a report by Frost and Sullivan in 2007, revenues for non-AFIS fingerprint devices in notebook PC's and wireless devices is anticipated to grow from $148.5 million to $1588.0 million by 2014, a compound annual growth rate of 40.3% [1]. The AFIS market has a compound annual growth rate of 15.2% with revenues of $445.0 million in 2007. With the development of mobile applications in a number of different market segments, such as healthcare, retail, and law enforcement, this paper analyzed the performance of fingerprints of different sizes, from different sensors...ICBR Databases
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- 1. 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
- 2. Agenda Motivation – why are we doing this? Data Collection Results Questions and Further Research Comments / Questions
- 3. 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
- 4. 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
- 5. Data Collection Setup – Sensor Specifications
- 6. 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
- 7. Methodology - Timing Throughput is important in an operational setting What is the impact of force on timing
- 8. Methodology – Comfort Level Likert scale
- 9. Methodology
- 10. 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
- 11. 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
- 12. Results Sample description Force banding Performance Throughput Comfort levels
- 13. Results - Subjects Age Range Distribution Subjects Distribution
- 14. Results – Auto-capture Force Distribution
- 15. Results – Force Distribution Samples
- 16. Results – Image Quality Scores (AWARE) Descriptive Statistics of Image Quality Score
- 17. 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
- 18. 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
- 19. 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)
- 20. Results – Tukey’s HSD Test Auto-capture image quality scores were similar to 1.5 and 2.5 N
- 21. Results – Detection Error Tradeoff (DET)
- 22. Results – False Reject Rate (FRR) at Fixed FAR 0.01% FRR Across Force Levels
- 23. Results – Biometric Subsystem Processing Time
- 24. Results - Comfort Level Comfort Level Average
- 25. 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.
- 26. Any Questions? Follow the discussion on the research blog after the conference www.bspalabs.org/
- 27. 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