BIOMETRICS LAB
Biometric Standards, Performance and Assurance Laboratory
Department of Technology, Leadership and Innovati...
RESEARCH QUESTIONS
• How does Henry Classification differ
across finger locations?
• Does image quality differ across fing...
RESPONSIVE
• Further the understanding of Henry
Classifications
• Refine zoo plot analysis
• Support ideal finger theories...
SENSOR
• Identix DFR-2080
• Optical touch
• 500 dpi
• 15mm x 15mm
platen
SUBJECTS
• Examined a subject pool of 190 users.
• Collected from a multi-sensor study
• Many subjects were missing images...
SUBJECT SUBSET
• 169 subjects
• 118 male, 49 female
• 148 office workers, 16 manual laborers
61545349474340383736353433313...
SUBJECT SUBSET
• Each subject provided six successful
impressions for each of:
• Left index
• Right index
• Left middle
• ...
HENRY CLASSIFICATIONS
• Neurotechnology Megamatcher v4.0.0
• Whorl
• Left Slant Loop
• Right Slant Loop
• Tented Arch
• Pl...
HENRY CLASSIFICATIONS
Henry LI LM RI RM
# % # % # % # %
Whorl 322 31.6 235 23.0 301 29.5 212 20.8
Left Slant
Loop
421 41.3...
LEFT INDEX
Henry LI LM RI RM
# % # % # % # %
Whorl 322 31.6 235 23.0 301 29.5 212 20.8
Left Slant
Loop
421 41.3 675 66.2 2...
LEFT MIDDLE
Henry LI LM RI RM
# % # % # % # %
Whorl 322 31.6 235 23.0 301 29.5 212 20.8
Left Slant
Loop
421 41.3 675 66.2 ...
RIGHT INDEX
Henry LI LM RI RM
# % # % # % # %
Whorl 322 31.6 235 23.0 301 29.5 212 20.8
Left Slant
Loop
421 41.3 675 66.2 ...
RIGHT MIDDLE
Henry LI LM RI RM
# % # % # % # %
Whorl 322 31.6 235 23.0 301 29.5 212 20.8
Left Slant
Loop
421 41.3 675 66.2...
IMAGE QUALITY
• Aware M1 Pack v3.0.0
• Fingerprint image quality is a prediction
of a matching software’s performance
IMAGE QUALITY
RIGHT INDEX
RIGHT MIDDLE
LEFT INDEX
LEFT MIDDLE
MINUTIAE COUNT
• Aware M1 Pack v3.0.0
• The count of local ridge characteristics
MINUTIAE COUNT
LEFT INDEX/MIDDLE
RIGHT INDEX/MIDDLE
IMAGE QUALITY AND MINUTIAE
ZOO PLOT
• Neurotechnology Megamatcher v4.0.0
• Performix v3.1.9
• Calculated by a minutiae-based matcher
ZOO PLOT OVERVIEW
• Maps the
relationship
between a user’s
genuine and
imposter match
results defines four
additional clas...
CLASSIFICATIONS OF ANIMALS
• Chameleons always appear similar to
others, receiving high match scores for
all verifications...
CLASSIFICATIONS OF ANIMALS
• Doves are the best possible users in
biometric systems. They matching well
against themselves...
ADVANTAGES OF ZOO PLOTS
OVER ROC/DET CURVES
• Traditional methods of evaluation focus
on collective error statistics such ...
ADVANTAGES OF ZOO PLOTS
OVER ROC/DET CURVES
• In many real world situations it has been
observed that user groups performa...
ZOO PLOT
ZOO PLOT
Worms
Phantoms
Chameleons
Doves
ZOO ANIMAL BY LOCATION
RIGHT INDEX
RIGHT INDEX
RIGHT INDEX
LEFT MIDDLE
FUTURE WORK
• Determine if these results hold true for
other fingerprint sensors
• Deeper analysis of the impact of poor
p...
CONTACT INFORMATION
• Michael Brockly
• Undergraduate Researcher at BSPA Lab
• mbrockly@purdue.edu
• Stephen Elliott PhD
•...
BIOMETRICS LAB
Biometric Standards, Performance and Assurance Laboratory
Department of Technology, Leadership and Innovati...
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(2011) Image Quality, Performance, and Classification - the Impact of Finger Location

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Presented at The 7th International Conference on Information Technology and Applications (ICITA 2011), Sydney Australia, 21 Nov - 24 Nov 2011.

The purpose of this presentation is to provide additional analysis of image quality and Henry Classification on Finger location on a single sensor. One hundred and sixty nine individuals provided six impressions of their left index, left middle, right index, and right middle fingers. The results show that there is significant difference in image quality, Henry classifications, and zoo animal distribution across the four finger locations under study. The results of this research show that location is an important consideration when developing enrollment best practices for single print systems.

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  • High frequency of chameleons and doves
  • High frequency of chameleons and doves
  • Dove has highest count for right index and right middle. Refer back to right index and right middle having best quality and left index and left middle having best minutiae. Very small amount of RI in worms but a large amount of chameleons.
  • Dove has highest count for right index and right middle. Refer back to right index and right middle having best quality and left index and left middle having best minutiae. Very small amount of RI in worms but a large amount of chameleons. High chameleons can cause high false accepts.
  • Dove has highest count for right index and right middle. Refer back to right index and right middle having best quality and left index and left middle having best minutiae. Very small amount of RI in worms but a large amount of chameleons. High chameleons can cause high false accepts.
  • Dove has highest count for right index and right middle. Refer back to right index and right middle having best quality and left index and left middle having best minutiae. Very small amount of RI in worms but a large amount of chameleons. High chameleons can cause high false accepts.
  • Dove has highest count for right index and right middle. Refer back to right index and right middle having best quality and left index and left middle having best minutiae. Very small amount of RI in worms but a large amount of chameleons. High chameleons can cause high false accepts.
  • (2011) Image Quality, Performance, and Classification - the Impact of Finger Location

    1. 1. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation IMAGE QUALITY, PERFORMANCE, AND CLASSIFICATION – THE IMPACT OF FINGER LOCATION Purdue University: Michael Brockly | Stephen Elliott
    2. 2. RESEARCH QUESTIONS • How does Henry Classification differ across finger locations? • Does image quality differ across finger locations? • Does minutiae count differ across finger locations • Does finger location impact performance?
    3. 3. RESPONSIVE • Further the understanding of Henry Classifications • Refine zoo plot analysis • Support ideal finger theories based on image quality and minutiae
    4. 4. SENSOR • Identix DFR-2080 • Optical touch • 500 dpi • 15mm x 15mm platen
    5. 5. SUBJECTS • Examined a subject pool of 190 users. • Collected from a multi-sensor study • Many subjects were missing images due to error, either data collection or Failure to Acquire (FTA) • Reduced the subject pool to 169 subjects to ensure equal numbers of fingers
    6. 6. SUBJECT SUBSET • 169 subjects • 118 male, 49 female • 148 office workers, 16 manual laborers 6154534947434038373635343331302928272625242322212019 40 30 20 10 0 Age Frequency User distribution of age
    7. 7. SUBJECT SUBSET • Each subject provided six successful impressions for each of: • Left index • Right index • Left middle • Right middle • 4,080 samples in total
    8. 8. HENRY CLASSIFICATIONS • Neurotechnology Megamatcher v4.0.0 • Whorl • Left Slant Loop • Right Slant Loop • Tented Arch • Plain Arch • Scar
    9. 9. HENRY CLASSIFICATIONS Henry LI LM RI RM # % # % # % # % Whorl 322 31.6 235 23.0 301 29.5 212 20.8 Left Slant Loop 421 41.3 675 66.2 259 25.4 46 70.0 Right Slant Loop 190 18.6 35 3.4 366 35.9 714 4.5 Tented Arch 54 5.3 46 4.5 24 5.2 5 2.8 Plain Arch 32 3.1 24 2.4 39 3.8 14 1.4 Scar 1 0.1 5 0.5 2 0.2 5 0.5
    10. 10. LEFT INDEX Henry LI LM RI RM # % # % # % # % Whorl 322 31.6 235 23.0 301 29.5 212 20.8 Left Slant Loop 421 41.3 675 66.2 259 25.4 46 70.0 Right Slant Loop 190 18.6 35 3.4 366 35.9 714 4.5 Tented Arch 54 5.3 46 4.5 24 5.2 5 2.8 Plain Arch 32 3.1 24 2.4 39 3.8 14 1.4 Scar 1 0.1 5 0.5 2 0.2 5 0.5
    11. 11. LEFT MIDDLE Henry LI LM RI RM # % # % # % # % Whorl 322 31.6 235 23.0 301 29.5 212 20.8 Left Slant Loop 421 41.3 675 66.2 259 25.4 46 70.0 Right Slant Loop 190 18.6 35 3.4 366 35.9 714 4.5 Tented Arch 54 5.3 46 4.5 24 5.2 5 2.8 Plain Arch 32 3.1 24 2.4 39 3.8 14 1.4 Scar 1 0.1 5 0.5 2 0.2 5 0.5
    12. 12. RIGHT INDEX Henry LI LM RI RM # % # % # % # % Whorl 322 31.6 235 23.0 301 29.5 212 20.8 Left Slant Loop 421 41.3 675 66.2 259 25.4 46 70.0 Right Slant Loop 190 18.6 35 3.4 366 35.9 714 4.5 Tented Arch 54 5.3 46 4.5 24 5.2 5 2.8 Plain Arch 32 3.1 24 2.4 39 3.8 14 1.4 Scar 1 0.1 5 0.5 2 0.2 5 0.5
    13. 13. RIGHT MIDDLE Henry LI LM RI RM # % # % # % # % Whorl 322 31.6 235 23.0 301 29.5 212 20.8 Left Slant Loop 421 41.3 675 66.2 259 25.4 46 70.0 Right Slant Loop 190 18.6 35 3.4 366 35.9 714 4.5 Tented Arch 54 5.3 46 4.5 24 5.2 5 2.8 Plain Arch 32 3.1 24 2.4 39 3.8 14 1.4 Scar 1 0.1 5 0.5 2 0.2 5 0.5
    14. 14. IMAGE QUALITY • Aware M1 Pack v3.0.0 • Fingerprint image quality is a prediction of a matching software’s performance
    15. 15. IMAGE QUALITY
    16. 16. RIGHT INDEX
    17. 17. RIGHT MIDDLE
    18. 18. LEFT INDEX
    19. 19. LEFT MIDDLE
    20. 20. MINUTIAE COUNT • Aware M1 Pack v3.0.0 • The count of local ridge characteristics
    21. 21. MINUTIAE COUNT
    22. 22. LEFT INDEX/MIDDLE
    23. 23. RIGHT INDEX/MIDDLE
    24. 24. IMAGE QUALITY AND MINUTIAE
    25. 25. ZOO PLOT • Neurotechnology Megamatcher v4.0.0 • Performix v3.1.9 • Calculated by a minutiae-based matcher
    26. 26. ZOO PLOT OVERVIEW • Maps the relationship between a user’s genuine and imposter match results defines four additional classes of worms, doves, chameleons, and phantoms
    27. 27. CLASSIFICATIONS OF ANIMALS • Chameleons always appear similar to others, receiving high match scores for all verifications. Chameleons rarely cause false rejects, but are likely to cause false accepts. • Phantoms lead to low match scores regardless of who they are being matched against; themselves or others.
    28. 28. CLASSIFICATIONS OF ANIMALS • Doves are the best possible users in biometric systems. They matching well against themselves and poorly against others. • Worms are the worst users of a biometric system. Where present, worms are the cause of a disproportionate number of a system’s errors.
    29. 29. ADVANTAGES OF ZOO PLOTS OVER ROC/DET CURVES • Traditional methods of evaluation focus on collective error statistics such as Equal Error Rates (EERs) and Receiving Operating Characteristic (ROC) curves. • These statistics are useful for evaluating systems globally, but ignore problems associated with individuals and subgroups of the population. The biometric menagerie is a formal approach to user-centric analysis.
    30. 30. ADVANTAGES OF ZOO PLOTS OVER ROC/DET CURVES • In many real world situations it has been observed that user groups performance varies based on any number of demographic factors. • Researchers and system integrators are interested in identifying which of these groups are performing poorly as they may be causing a disproportionate number of verification errors.
    31. 31. ZOO PLOT
    32. 32. ZOO PLOT Worms Phantoms Chameleons Doves
    33. 33. ZOO ANIMAL BY LOCATION
    34. 34. RIGHT INDEX
    35. 35. RIGHT INDEX
    36. 36. RIGHT INDEX
    37. 37. LEFT MIDDLE
    38. 38. FUTURE WORK • Determine if these results hold true for other fingerprint sensors • Deeper analysis of the impact of poor performing animals
    39. 39. CONTACT INFORMATION • Michael Brockly • Undergraduate Researcher at BSPA Lab • mbrockly@purdue.edu • Stephen Elliott PhD • Associate Professor at BSPA Lab • elliott@purdue.edu
    40. 40. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation Questions?
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