Matching Randomly Acquired 

Characteristics in Footwear 

Impressions

Weiqing Chen, Martin Herman

Information Technology Laboratory, NIST
Purpose

• Obtain a quantitative similarity score for 

footwear impression comparisons.
Class characteristics

Vans 11 Skechers 9.5 Vans 10.5 Vans 11
Class Characteristics

Vans 11 Vans 11

• Design(pattern)

• Size

• General wear
Randomly Acquired Characteristics(RACs)

Definition

A RAC feature is a feature on a shoe 

outsole resulting from random events. 

• RACs are not replicated in 

every impression.

• Research has demonstrated 

that the chance duplication of 

even one characteristic’s 

position, orientation, shape 

and size on another shoe of the 

same size and design would be 

rare.
Randomly Acquired Characteristics(RACs)

Types

Include but not limited 

to:

• Cuts

• Scratches

• Tears

• Holes

• Foreign objects

• Abrasions

• Debris
Workflow of RACs Comparison
Global Registration

Test impression Questioned impression Global registration 

result
Local Registration

Global registration 

result

Test Questioned

Test Questioned

Local 

registration
Registration Methods

Global Registration

• Principal Axes and Mutual Information

• Point Configuration Methods

Local Registration

• Mutual Information

I X; Y = H X + H Y − H X, Y

I X; Y = ෍

x

෍

y

p x, y log

p(x, y)

p x p(y)
Impression comparison based on RACs

Test Questioned1 (Q1)

(Mated)

Questioned (Q2)

(Non-mated)
Impression comparison based on RACs

Test

Q1

(Mated)

Q2

(Non-mated)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
RACs Comparison

Comparison metric

• Normalized cross correlation

r =

σm σn(Amn − Aҧ)(Bmn − Bത)

(σm σn(Amn − Aҧ)

2)(σm σn(Bmn − Bത)2)

Comparison scores

RAC No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Average

Q1 vs 

Test

0.9400 0.8917 0.8595 0.8617 0.7164 0.8815 0.9105 0.8687 0.8212 0.9152 0.9110 0.8790 0.7357 0.9155 0.6926 0.8534

Q2 vs 

Test

0.4039 0.5283 0.4779 0.8714 0.8861 0.3849 0.3624 0.6873 0.3329 0.8443 0.5301 0.4497 0.3954 0.8281 0.2596 0.5495
Impression comparison based on RACs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Test

Q1

(Mated)

Q2

(Non-mated)
Performance of the comparison algorithm
Performance of the comparison algorithm
Performance of the comparison algorithm
Conclusion 

Include more information of RACs

RAC comparison score can help us to give a conclusion of the comparison 

between questioned impression and known impression according to 

SWGTREAD range of conclusions scale. The comparison approach used in this 

presentation is only based on the pixel values of corresponding pixels. The 

other information of the RACs such as shape, orientation, size will also be 

incorporated into the similarity score. 

Find better methods to combine similarity scores of all 

RAC pairs into a final score

Different RACs have different importance to the final score due to their 

different size, shape complexity, orientation and etc. 
Reference 

1. William J. Bodziak. Forensic footwear evidence. CRC Press. 2017

2. https://www.swgtread.org/standards/published-standards

3. Yoram Yekutieli, Yaron Shor, etc. Expert assisting computerized 

system for evaluating the degree of certainty in 2D shoeprints. 

Technical Report. 2016.

4. Josien P. W. Pluim, J. B. Antoine Maintz, etc. Mutual information 

based registration of medical images: a survey. IEEE Transactions on 

Medical Imaging. 2003. 

5. Jacueline A. Speir. A quantitative assessment of shoeprint 

accidental patterns with implications regarding similarity, 

frequency and chance association of features. Technical report. 

West Virgia University. Feb. 2018.
Acknowledgement 

• We wish to thank Gunay Dogan, Hari Iyer, Steven Lund, Yooyoung 

Lee for helpful discussions.

• We wish to thank our NIST colleagues for donating so many shoes to 

our team.