(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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40

30

20

10

0

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• mbrockly@purdue.edu

• Stephen Elliott PhD• Associate Professor at BSPA Lab• elliott@purdue.edu

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

Questions?