(2011) Image Quality, Performance, and Classification - the Impact of Finger Location
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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
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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.
Transcript of (2011) Image Quality, Performance, and Classification - the Impact of Finger Location
BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation
IMAGE QUALITY, PERFORMANCE, AND CLASSIFICATION – THE IMPACT OF FINGER LOCATIONPurdue University: Michael Brockly | Stephen Elliott
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?
RESPONSIVE
• Further the understanding of Henry Classifications
• Refine zoo plot analysis• Support ideal finger theories based on
image quality and minutiae
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 due
to error, either data collection or Failure to Acquire (FTA)
• Reduced the subject pool to 169 subjects to ensure equal numbers of fingers
SUBJECT SUBSET
• 169 subjects• 118 male, 49 female• 148 office workers, 16 manual laborers
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Age
Frequency
User distribution of age
SUBJECT SUBSET
• Each subject provided six successful impressions for each of:• Left index• Right index• Left middle• Right middle
• 4,080 samples in total
HENRY CLASSIFICATIONS
• Neurotechnology Megamatcher v4.0.0
• Whorl• Left Slant Loop• Right Slant Loop• Tented Arch• Plain Arch• Scar
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
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
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
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
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
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 classes of worms, doves, chameleons, and phantoms
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.
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.
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.
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.
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 performing animals
CONTACT INFORMATION
• Michael Brockly• Undergraduate Researcher at BSPA Lab• [email protected]
• Stephen Elliott PhD• Associate Professor at BSPA Lab• [email protected]
BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation
Questions?
