{ Shahram Orandi 

September 28, 2011 

The NIST Fingerprint Image 

Compression Study 
Key team activities: 

 Simulation modeling 

Standards & conformance work 

Algorithm R&D 





Primary focus for the past year has been the 

Fingerprint Compression Study, which ties 

together all the above competencies. 

Before we talk about the study, What is compression? 
Background info... What is compression? 

 A method of encoding information in a way that it uses fewer 

bits than the original representation, and thereby becomes 

smaller in size. 

 Two approaches to compression: 

1. Lossless 

2. Lossy 

Examples of each... 
Lossless Compression: 

Compressed image is as good as the original... Nothing is lost.

Original uncompressed size: 2,095,104 bytes 

Compressed size: 251,171 bytes (1/8th of original) 

This is an example of lossless compression. 

What if we’re willing to lose a bit of fidelity, to gain even more compression? 
Lossy compression (magnified): 

We can specify how much effective compression we’re 

willing to gain at the price of how much information we’re 

willing to lose. The price is fidelity loss. 

Same is true with fingerprints 

39,472 bytes 

( 1/53rd of original)

16,045 bytes 

(1/130th L of original)
Example of fingerprints with lossy compression: 

So why lossy compression for fingerprints? 

Lossless 1/100th of 

original 

(100:1) 

1/200th of 

original 

(200:1) 

1/400th of 

original 

(400:1) 

1/800th of 

original 

(800:1) 
So why go Lossy? 

Lossy compression will be a critical part of modern 

biometric systems processes to help make the most out of our 

existing resources, as well as keep up with an ever increase 

load. 

Speaking of ever increasing load... 
Changes coming...

 Currently, bulk of legacy biometric systems 

operation is based on 500ppi fingerprint 

processing with lossy compression. 

 NGI as well as other modern systems will 

support higher resolution (1000ppi). 

1000ppi will quadruple the load 
Twice as much data in each direction means four times the data total. 

Not all bad news... 1000ppi yields much better images? 

1 2 

3 4 

1 2 3 4 

5 6 7 8 

9 10 11 12

13 14 15 16

This quadrupling of data reinforces why we will have to continue to 

rely on lossy compression to keep data footprint managable. 
500ppi 1000ppi 

Actual Specimen Images: 500ppi vs. 1000ppi 

So how do we get there? 
So how do we get there? 

Lossless is easy: You don’t do anything special, the algorithm 

ensures what goes in will be the same as what comes out later. 

Lossy is tougher: You need a ‘recipe’ to provide your algorithm 

on how much its allowed to filter out when compressing your 

data. 

For 500ppi (legacy) the IAI conducted a study in 1994 to establish 

the basis of what became the recipe on lossy fingerprint 

compression using the WSQ algorithm. 

In the NIST compression study we tried to replicate the IAI study 

at 1000ppi . 

What happened in the IAI study?
Goals of the NIST study... 

Compression Level Result Code Total

1 : no noticeable degradation 2: degradation of non-Galton 3: degradation of Galton details

5:1 202 0 0 202 

10:1 200 0 0 200 

15:1 195 7 (3.4%) 0 202

20:1 37 159 0 196 

Total 634 166 0 800 

The 1994 IAI Study in a nutshell: 

• 100 fingerprints cards selected from Illinois State Police 

• 4 rolled prints selected from each card yielding 400 prints. Selection 

was representative by pattern class (65%L/29%W/6%A), 86%M / 

%14F 

• Each of the 400 prints compressed randomly at 5:1, 10:1, 15:1 or 

20:1... Yielding about 100 prints at each of the four ratios 

• Two professional examiners independently compared the 400 

compressed images to the original, and rated the degradation (1-3 

scale) 

• If the two examiners were deadlocked, a third examiner would break 

the deadlock 
The strategic goals of the NIST compression study: 

 Replicate the best practices of the IAI study, for 1000ppi this 

time, and improve where we can. 

 Large scale test of MITRE guidance (MTR-04B0000022) 

 Test wider range of compression ratios (from 2:1 to 38:1) than 

the original IAI study 

 Utilize more image sets, test wider range of impression types. 

rolled, flat, slaps, both card scan and live scan to more 

Details of the study... 
2010 Compression Study Details: 

 Utilized 3 examiners at the same time 

 Utilized 84000 image pairs 

 Each pair was guaranteed to be observed by exactly 3 unique 

examiners. 

 Examiners were shown an image pair, one lossy-compressed and 

one original. 

 First, examiners asked to make an ident decision on the pair of 

images. 

 Next, examiners asked for a subjective evaluation of compression 

fidelity loss between the two images. 

 Examiners were not told which image was compressed. 

 Examiners were provided equal and identical calibrated stations. 

Investigative goals... 
The NIST compression study is very large in scope so it has been 

partitioned into several reports. 

First is NISTIR-7778 which is the topic of this presentation, was 

released on May 25, 2011. 

Four investigative goals for NISTIR-7778: 

1: Validate 15:1 target compression ratio 

2: Examine image degradation relative to compression ratio 

3: Assess impact of compression on identification error rates 

4: Examine compression anomalies relative to impression type 

So what did we find? Results... 
Investigative Goal 1: Validate 15:1 target compression ratio 

Finding: Study shows that 15:1 compression of rolled-to-rolled ink card 

scan imagery at 1000ppi falls just a bit outside of the quality criteria the 

IAI used to establish guidance at 500ppi (4% mark). 

Going Forward: 15:1 is a viable 

compression goal, but a lower 

compression ratio may offer 

benefits. A final recommendation 

will be made after other segments 

of compression study complete.

0

20

40

60

80

100

120

140

160

180

200

Compression Anomalies (Ink Rolled to Ink Rolled, Mated Pair) 

1:1 2:1 5:1 7:1 10:

1

12:

1

15:

1

17:

1

20:

1

22:

1

26:

1

30:

1

34:

1

38:

1

Level 2 and 3 detail degradation 0 0 0 0 0 0 0 0 0 0 1 0 0 3

Level 3 detail degradation 2 0 4 4 6 10 8 16 11 23 28 29 32 36

Some benign degradation 33 34 45 67 92 107 112 125 133 131 126 133 141 134

No visible degradation 165 166 151 129 102 83 80 59 56 46 45 38 27 27

IAI Cutoff (3.4%, adjusted) 8 8 8 8 8 8 8 8 8 8 8 8 8 8 Observation Count
Investigative Goal 2: Examine image degradation trends relative 

to increasingly higher compression ratios 

Finding: Examination of standardized scores demonstrates that 

perceived image quality trends negatively with increased 

compression across all image types. Also show that certain 

impression types degrade more aggressively are than others. 

Going forward: Possible 

exploration of dynamic 

compression strategy 

based on image type. 

-25.00

-20.00

-15.00

-10.00

-5.00

0.00

5.00

1:12:15:17:1

10:112:115:117:120:122:126:130:134:138:1

Z-Scores 

Compression Ratio 

Z-Value Trends (Ink Card Scan) 

Linear (Rolled to Rolled,

Mated Pair)

Linear (Rolled to Rolled,

Non-Mated Pair)

Linear (Rolled to Flat,

Mated Pair)

Linear (Rolled to Flat,

Non-Mated Pair)

Linear (Flat to Rolled,

Mated Pair)

Linear (Flat to Rolled,

Non-Mated Pair)

Linear (Flat to Flat,

Mated Pair)

Linear (Flat to Flat, NonMated Pair)

Linear (Slap to Slap,

Mated Pair)

Linear (Slap to Slap,

Non-Mated Pair)
Investigative Goal 3: Assess impact of compression on 

identification error rates 

Finding: Analysis of the observed error rates demonstrates that 

while expert fingerprint examiners can identify image 

degradation and feature loss at relatively low compression rates, 

their ability to make an identification does not appear to be 

measurably impacted. 

Going forward: This was welcome 

news. The dirty-windshield 

analogy lives, even at 38:1! 

0 5 10 15 20 25 30 35 40

-0.01

0

0.01

0.02

0.03

0.04

0.05

Error Rates and 95% CI

 Inked Flat to Roll

Compression Ratio (x:1)

Error Rate
Investigative Goal 4: Examine compression anomalies relative to 

impression type 

Finding: Reinforced some impression types being impacted more than 

others... Also shows (for the first time?) statistical proof that a little 

compression can actually help ‚improve‛ the image.

Going forward: This 

Improvement effect is 

currently being examined 

further in the scope of latent 

image analysis.

-20.00

-15.00

-10.00

-5.00

0.00

5.00

1:12:15:17:1

10:112:115:117:120:122:126:130:134:138:1

Z-Scores 

Compression Ratio 

Computed Z-Scores by Ratio (Ink Card Scan) 
Where are we on the big NIST Compression Study road map? 

(Done) NISTIR 7778- Effects of JPEG 2000 Image Compression on 1000ppi 

Fingerprint Imagery (Lossy compression) 

 

(En Route) NISTIR 7779- A Survey of Lossless Compression Algorithms for 

1000ppi Fingerprint Imagery Data. 

 

(Analysis Started) NISTIR 7780- Effects of JPEG 2000 Image Compression on 

Latent 1000ppi Fingerprint Imagery 

 

(Analysis Started) NISTIR 7781: Spectral and PSNR Comparison of WSQ and 

JPEG 2000 Compression of 500ppi Fingerprint Imagery. 

 

(Data collected) NISTIR 7782: Effects of JPEG 2000 Image Compression for 

1000ppi Fingerprint Imagery on Legacy 500ppi Processing 

 

(Started) Special Publication 500-289: Compression Guidance for Fingerprint 

Imagery at 1000ppi. This is a critical item which will bring everything together 

Stay tuned... http://fingerprint.nist.gov
Q & A? 

Contact: 

Shahram Orandi 

sorandi@nist.gov

301-975-3261 
NIST Fingerprint Compression Study 

Supplemental Slide 

Judgment Criteria for IAI 1994 Study: 

1.

2.

3.

No noticeable reduction in image quality 

Slight reduction in image quality which may interfere with an identification based on 

poroscopy, ridgeology, or other non-Galton details. 

Noticeable reduction in image quality which may interfere with an identification 

based on the Galton details. 

Judgment Criteria for 2010 Compression Study: 

1. No apparent image quality degradation and the quality of Level II(2) and Level III(3) 

detail in either image should not cause any difficulty in reaching a conclusive decision 

of identification or exclusion. 

A noticeable degradation in the quality of Level II(2) or Level III(3) detail in either 

image, but not enough to have a negative impact on reaching a conclusive decision of 

identification or exclusion, though the amount of time to reach a decision may 

increase. 

Level III(3) detail quality diminished in either image to the extent that a Level III(3) 

identification is questionable or not possible, and/or is significantly more difficult. 

Level II(2) detail quality diminished in either image to the extent that a Level II(2) 

identification becomes questionable or not possible, and/or is significantly more 

difficult. 

2.

3.

4.
2010 Compression Study Test Tool (aka ‚FIXT‛) UI