Metrics for Enhancement of Latent 

Fingerprint Images

Mary Theofanos

Andrew Dienstfrey

Specific hardware and software products identified in this presentation were used in order to perform the evaluations described. 

In no case does such identification imply recommendation or endorsement by the National Institute of Standards and Technology, 

nor does it imply that the products and equipment identified are necessarily the best available for the purpose
The Value of Image Enhancements

What AFIS sees
Goals

– To characterize the effect of image processing and enhancement that 

occur between obtaining the latent fingerprint image from the crime scene 

and identifying features of the print for data matching against the database

– Adoption of uniform and enforceable best practices
Analysis of Latent Fingerprint Image Enhancement

What is the image enhancement decision making process?

• Image enhancement workflow 

What are these enhancements? What do they look like?

• New database of triples: latent images, enhanced images, metadata

What are these enhancements really? Are they reproducible?

• Mine the metadata for most common enhancement commands

• Universal (i.e. mathematical) description the underlying algorithm

Where are the metrics?

• Modifications to SIVV to customize for latent image characteristics

• Investigate application independent image metrics
There is a consistent process

Contribution of:

• Brian Stanton— NIST

2012-05-25T22:07:53-05:00 File NIST Study Images 

057 - V700.tif opened

Open C:\Users\matt\Desktop\2012 NIST Image 

Study\NIST Image Scans - Both copies\NIST Study Images 057 -

V700.tif

Rectangular Marquee

Set Selection To: rectangle

Top: 0.347 inches

Left: 0.124 inches

Bottom: 1.477 inches

Right: 1.167 inches

Crop

Crop

2012-05-25T22:08:06-05:00 File 

C:\Users\matt\Desktop\2012 NIST Image Study\NIST Image Scans -

Both copies\NIST Study Images 057 - V700.tif saved

2012-10-26T08:32:32-05:00 File NIST Study Images 

057 - V700.tif opened

Open C:\Users\dlwitzke.FORAY\Desktop\NIST 

Developed\NIST Study Images 057 - V700.tif

Select blue channel

Select RGB channel

Select blue channel

Extract Channel

Convert Mode To: grayscale mode

Chromatic FFT

Filter Using: “Chromatic FFT...”

Shadows/Highlights

Shadow/Highlight Shadow: Parameters

Amount: 35%

Tone Width: 50%

Radius: 30

Highlight: Parameters

Amount: 68%

Tone Width: 67%

Radius: 30

Black Clip: 0.01

White Clip: 0.01

Contrast: 36

Brightness: -24

Apply Image

Apply Image With: calculation

Source: current channel

Calculation: overlay

With Preserve Transparency

Apply Image

Apply Image With: calculation

Source: current channel

Calculation: screen
The process needs a trained examiner 

1 Address Color 2 Address Contrast 3 Fine Tune Image

• Contrast and Consistency are critical across image

• Clarity is crucial

• Decision points– must have more than sufficient number of ridge 

characteristics for individualization

• Enough information for confidence

• All about tracing points to get from one place to another 
Some Actions Create Artifacts 

1. Resampling 

2. Interpolation 

3. Rotation 

4. Levels

5. Curves

6. Dodge and Burn
What is the image enhancement decision making process?

• Image enhancement workflow 

What are these enhancements? What do they look like?

• New database of triples: latent images, enhanced images, metadata

What are these enhancements really? Are they reproducible?

• Mine the metadata for most common enhancement commands

• Universal (i.e. mathematical) description the underlying algorithm

Where are the metrics?

• Modifications to SIVV to customize for latent image characteristics

• Investigate application independent image metrics

Outline
What are Latent Fingerprint Enhancements?

New database of > 150 latent fingerprint images

• Different fingerprint development techniques

• Each image has enhanced counterpart

• PS utility logs metadata describing enhancement process

Let’s have a look...

—Bichromatic Powder —Black Ink —Silver Mag Powder

—Bichromatic Mag Powder —Ninhydrin —White Powder

Create a 

controlled

workspace.
Enhancement Triple: Original, Enhancement, Metadata

Original Enhanced

Metadata

2012-05-26T16:13:11-05:00

File NIST Study Images 158 - V700.tif opened

Black & White

Black & White Preset Kind: Custom

red: -12

yellow: -68

green: 300

⁞

Invert

⁞

Burn

⁞

Bi-Chromatic

Mag Powder
Enhancement Triple: Original, Enhancement, Metadata

Original Enhanced

Metadata

2012-05-25T22:43:54-05:00

File NIST Study Images 095 - V700.tif opened

⁞

Levels Preset Kind: Custom

Adjustment: levels adjustment list

levels adjustment

Channel: composite channel

Input: 0, 240

Gamma: 0.16

Deselect

⁞

Silver Mag

Powder
Enhancement Triple: Original, Enhancement, Metadata

Original Enhanced

Metadata

2012-05-25T22:37:31-05:00

File NIST Study Images 080 - V700.tif opened

⁞

Calculations

MakeNew: channel

Using: calculation

Source: current channel

Calculation: multiply

Opacity: 50%

Source2: current channel

Calculations

Extract Channel

⁞

Ninhydrin
What are Latent Fingerprint Image Enhancements?

• The short answer is, “Indispensable”.

• Of course this is not enough...

... ...
Latent Fingerprint Image Enhancement Database

Can one provide a basis for rules of evidence?*:

• “Nothing material or essential has been added to the original image.”

• “Nothing material or essential has been removed from the original image.”

*Robinson, Crime Scene Photography 2

nd Edition, (2010) pg. 549

New latent fingerprint database records

• Representative mappings: latents to enhancements

• Representative usage of Adobe Photoshop

Contribution provided by:

• David “Ski” Witzke, Foray Technologies

• Matt Schwarz, Schwarz Forensics
What are these enhancements? What do they look like?

• New database of triples: latent images, enhanced images, metadata

What are these enhancements really? Are they reproducible?

• Mine the metadata for most common enhancement commands

• Universal (i.e. mathematical) description the underlying algorithm

Where are the metrics?

• Modifications to SIVV to customize for latent image characteristics

• Investigate application independent image metrics

Outline
Data Mining the Database

Parsing hierarchy: Line < Root < Operation
Distribution of Enhancement Command Usage
New Data to Inform Answers to Natural Questions

Old Questions

Q: Which PS commands are important?

A: Look at the graph.

Q: Are latent fingerprint enhancements reproducible?

A: Not pixel-for-pixel using the metadata logs

— “Burn and Dodge” are used a total of 130 times in 7 out of the 39 images.

New Questions

What are the PS commands? PS-independent specifications are desired.

RGB-to-Gray is used 39/39 times.

— Different algorithms documented in practice: linear, affine, non-linear

— PS adds a (1-bit) random number to its output! 

Contribution of:

• Peter Bajcsy, Joe Chalfoun, and David Nimorwicz — NIST

• Julian Lee — SURF Student Researcher, University of MD

Dither!
Part II: Analysis of Image Enhancement

What are these enhancements? What do they look like?

• New database of triples: latent images, enhanced images, metadata

What are these enhancements really? Are they reproducible?

• Mine the metadata for most common enhancement commands

• Universal (i.e. mathematical) description the underlying algorithm

Where are the metrics?

• Modifications to SIVV to customize for latent image characteristics

• Investigate application independent image metrics
What do Latent Fingerprint Image Enhancements do?

SIVV = Spectral Image Validation/Verification

*Libert, et. al., “A 1D Spectral Image Validation/Verification Metric for Fingerprints”, NISTIR-7599 (2009)
Can SIVV Measure the Value of an Enhancement?

Findings to date (research is ongoing):

• SIVV feature emerges as result of enhancement

• Can we develop a metric out of this?
Now for Something Completely Different

Adobe Photoshop is a double-edged sword

Pros Cons

Familiar to latent community Visually motivated

Ease of use Algorithmic control is limited

Reproducibility is questionable

Explore alternative tools: Matlab, Interactive Data Language (IDL)

Investigate application independent (i.e. “generic”) image metrics
Alternative Tools and Metrics

• Many more details in NISTIR under review.

• Contribution of: Al Carasso— NIST
Conclusions

Accomplishments:

• Defined workflow

• Well characterized database/workspace

• Initiated systematic analysis of enhancement procedures

• Begun metrical analysis

Next Steps

• Refine and document the above

Future Efforts

• Guidelines/Best Practices and Document Standards

• Augment analysis to include matcher technologies