Visual Processing in Fingerprint Experts and Novices

Visual Processing in Fingerprint Experts and Novices

Tom BuseyIndiana University, Bloomington

John VanderkolkIndiana State Police, Fort Wayne

www.indiana.edu/~busey/

How Do Experts Make Identifications?

Easy

Mat

chH

ard

Mat

ch

What Perceptual Abilities Support Expertise?

• Experts may learn the relevant features or dimensions, supported by naming

• Tune detectors to specific characteristics of features (exclude noise)

• Integrate information over larger regions of space• Superior visual memory to support matching from one

print to the other

Study Fragment

one second

Mask

Either 200 ms or 5200 ms

Test Images

Until Response

Testing Fingerprint Expertise:X-AB Sequential Matching Task

Study Image1 Second

Mask 200 or 5200Milliseconds

Test ImagesUntil Response

example stimulus pairs:

At Study:

• Study image is rotated up to 90° in either direction and brightness is jiggled up or down

• Reduces reliance on low-level features like orientation of ridge flow or image brightness

At Test:

• Two image manipulations designed to simulate latent prints– Added noise– Partial masking

Added Noise

Partial Masking

Includes combinations:

Image Degradations at Test

Clear FragmentsPartially-Masked Fragments

Partially-Masked FragmentsPresented in NoiseFragmentsPresented in Noise

0.5

0.6

0.7

0.8

0.9

1.0

Full Image Partial Image

Experts- Short Delay

No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0


Experts- Long Delay

No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0


Novices- Short Delay

No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0


Novices- Long Delay

No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0



No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0


Experts- Long Delay

No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0



No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0


Novices- Long Delay

No NoiseNoise Added

Percent Correct

Image Type

Behavioral Data

Full Images Partial Images

Full Images in Noise

Partial Images in Noise

Experts: No effect of delay, interaction between noise and partial masking.

0.5

0.6

0.7

0.8

0.9

1.0



No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0


Experts- Long Delay

No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0



No NoiseNoise Added

Percent Correct

Image Type

0.5

0.6

0.7

0.8

0.9

1.0


Novices- Long Delay

No NoiseNoise Added

Percent Correct

Image Type

Behavioral Data

Full Images Partial Images

Full Images in Noise

Partial Images in Noise

Experts: Doing really well in the Full Image condition innoise. Configural processing?

Partial Masking

Semi-TransparentMasks

Fingerprint Partially MaskedFingerprints

SummationRecovers Original

Fingerprint

orig

inal

inve

rse

Logic of Partial Masking

Partially MaskedFingerprints

Linear SummationRecovers Original

Fingerprint

One Half

One Half

Both Halves

Evidence for Configural Processing: Multinomial Modeling

To test for configural processing, we can use the accuracy rate in the partial image condition to make a prediction for the full image condition, assuming no configural processing. If performance in the full image condition exceeds the prediction, we have evidence that is consistent with configural processing.


To test for configural processing, we can use the accuracy rate in the partial image condition to make a prediction for the full image condition, assuming no configural processing. If performance in the full image condition exceeds the prediction, we have evidence that is consistent with configural processing.

Based on a Multinomial Processing Tree implimentation of a probability summation prediction.

0.5

0.6

0.7

0.8

0.9

1.0


Experts

No NoiseNoise AddedNo Configurality

Percent Correct

Image Type


Experts in noise: We predict performance in the full image condition to be about 75% correct. Instead it is around 90%. Experts are doing better with the whole image than we predict they would do based on partial-image performance. This is configural processing at work.

Configural Processing in Faces: The ‘Thatcher Illusion’

(Thomson, 1980)

Features are perceived

individually, image looks ok.


(Thomson, 1980)



Features are perceived in

context, image looks grotesque.


(Thomson, 1980)






(Thomson, 1980)





EEG Recording Basics• Record from the surface

of the scalp• Amplify 20,000 times• Electrical signals are

related to neuronal firing, mainly in post-synaptic potentials in cortex.

• Very small signals, very noisy data.

EEG and Configural ProcessingFaces produce a strong

component over the right hemisphere at about 170 ms after stimulus onset, which is called the N170. Inverted faces cause a delay of 10-20 ms in the N170.

Trained objects (Greebles) show a delay in the N170 component with inversion, but only after training.

Data from Rossion, Gauthier, Goffaux, Tarr & Crommelinck (2002)

Data from Rossion, Gauthier, Tarr, Despland, Bruyer, Linotte & Crommelinck (2000)

Fingerprints have an orientation

• Experts always view fingerprints with the tip pointing upwards.

An Obvious Experiment:

Show upright and inverted fingerprints to Fingerprint examiners and novices. If experts process fingerprints configurally, we should see a delayed N170 to inverted fingerprints.

Also test faces to replicate the face inversion effect in our subjects. Test both identification and categorization tasks.

-4

-2

0

2

4

6

8

10

0 100 200 300 400

Experts- Identification Task

Upright FingerprintInverted FingerprintUpright FaceInverted Face

Amplitude (µV)

time (ms)

Faces: LatencyDifference (p<0.05)

Right Hemisphere- T6

-4

-2

0

2

4

6

8

10

0 100 200 300 400



Amplitude (µV)

time (ms)

Fingerprints: LatencyDifference (p<0.05)



-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

0 100 200 300 400

Novices- Identification Task


Amplitude (µV)

time (ms)



Fingerprints: No LatencyDifference

(curves are virtually identical, n.s.)

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

0 100 200 300 400

Novices- Identification Task


Amplitude (µV)

time (ms)



-4

-2

0

2

4

6

0 100 200 300 400

Experts- Categorization Task


Amplitude (µV)

time (ms)



-4

-2

0

2

4

6

0 100 200 300 400

Experts- Categorization Task


Amplitude (µV)

time (ms)




-8

-6

-4

-2

0

2

4

6

8

10

12

14

0 100 200 300 400

Novices- Categorization Task


Amplitude (µV)

time (ms)



Fingerprints: No LatencyDifference

(curves are virtually identical, n.s.)

-8

-6

-4

-2

0

2

4

6

8

10

12

14

0 100 200 300 400

Novices- Categorization Task


Amplitude (µV)

time (ms)



The Bottom Line• Experts perform better than Novices in all conditions• Better in noise• Better at longer delays• Really good when have both halves present at test• Attributed to configural processing• Supported by EEG recording

– Only for Experts show an effect of inversion on the N170 when viewing fingerprints

• Places strong constraints on the locus of expertise– Perceptual in nature- N170 reflects late stages of perceptual processing– Can't be due to demand characteristics

• Lots of plausible perceptual and cognitive models that suggest that this kind of perceptual expertise would help in actual fingerprint examinations

Thank You

Summary of ExperimentsFingerprint experts demonstrate strong performance in an X-AB matching task, robustness to noise and evidence for configural processing when stimuli are presented in noise. This latter finding was confirmed using upright and inverted fingerprints in an EEG experiment. Experts showed a delayed N170 component for inverted fingerprints in the same channel that they show a delayed N170 for inverted faces. Experts appear to be processing upright fingerprints in part using configural processing, which stresses relational information and implies dependencies between individual features. In the case of fingerprints, configural processing may come from idiosyncratic feature elements instead of well-defined features such as eyes and mouths. -4

-2

0

2

4

6

8

10

0 100 200 300 400



Amplitude (µV)

time (ms)




Documents

Visual Processing in Fingerprint Experts and Novices