Evaluation of 3-D Fingerprint

Image Capture Devices

(1/14/08)

Dr. Jin Chu Wu 

Jinchu.wu@nist.gov

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Headline News

DHS announces foreign visitors to have 10

print biometric (instead of 2 index fingers) 

starting Thursday (November 29, 2007) at 

Dulles Airport. 

Washington's Dulles Airport on Thursday will 

begin a new DHS scheme to acquire ten

fingerprints from each visitor to the United 

States; system will be implemented at all land

crossings by the end of 2008. 

1. Time taken?

2. Flat vs. Rolled? 2
Question: Should we live with live scanners 

forever?

Answer: No! “There’s Gotta Be A Better Way”. 

Disadvantages of live scanners:

1. Noise from the valley due to hard pressure.

2. Noise from residues of previous usages.

3. Speed and flat of obtaining ten prints.

4. Distortion of the surface of fingers.

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Live scanners:

2-d object 

2-d image

3-D fingerprint image capture devices:

As a result, things are much more complicated.

Note: We are only interested in the 2-d rolledequivalent images for evaluation and 

interoperability purposes. 4

3-d object

3-d image

2-d rolled-equivalent image
1. A website has been set up at

http://fingerprint.nist.gov/3Dcapture

Accomplishments

to activate and facilitate all kinds of networking activities, 

such as discussion, making recommendations and 

suggestions, announcing the status and news, etc.

2. Established the framework of our project such as 

absolute measurement and relative measurement, etc.

3. Presented the framework of our project at the Biometric 

Consortium Conference in Baltimore in September 07.

4. Did numerous relative-measurement analyses on Phase 

I and Phase II devices from TBS. Praised by TBS CEO.

5. Communicated with FBI and other vendors.

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Compare with those collected from live 

scanners in terms of both accuracy and 

speed. 

1. Stipulate the specifications. 

2. Design targets to implement spec.

II. Relative Measurement - interoperability: 

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III. New Discoveries

Framework:

I. Absolute Measurement:
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I. Absolute Measurement

Here is a good start:

1. Personal Identify Verification (PIV) Image QualitySpecifications 

for Single Finger Scanners (July 2006) 

http://www.fbi.gov/hq/cjisd/iafis.htm

http://www.fbi.gov/hq/cjisd/iafis/piv/pivspec.htm

2. Test Procedures for Verifying Image Quality Requirements for 

Personal Identity Verification (PIV) Single Finger Capture Devices 

(December 2006) 

http://www.mitre.org/work/tech_papers/tech_papers_07/06_1384/

3. APPENDIX F: IAFIS IMAGE QUALITY SPECIFICATIONS (January 

1999)

http://www.fbi.gov/hq/cjisd/iafis/efts70/appendixf.htm
Geometric Accuracy !!Extracted from their publications!! 

pixel to pixel uniformity 

small area uniformity 

noise 

Fingerprint Image Quality

fingerprint gray range (dynamic range) 

fingerprint abnormalities

fingerprint sharpness & detail rendition

(Note: NFIQ, MITRE, Cogent, NEC, etc.)Partly extracted. 8

Spatial Frequency Response

CTF: Contrast (square wave) Transfer Function 

MTF: Modulation (sine wave) Transfer Function

Gray Level Uniformity

adjacent row, column uniformity
3-D Fingerprint Image Capture

The 2-D rolled-equivalent fingerprint image is 

obtained by converting the 3-D fingerprint 

image that is captured by the device.

Therefore, this is a process from 3-D 

manifold (simplified to be a surface) to 2-D 

plane rather than from 2-D plane to 2-D plane 

for live scanners.

As a consequence, things are much more

complicated. 

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Dynamic Range:

...... 10

Geometric Accuracy:

1. Systematic Errors and Measurement Errors, which are 

for both “2-D to 2-D” and “3-D to 2-D”.

2. Differential Geometry Issue, that is only for “3-D to 2-D”.

1. The probability distribution needs to be taken into 

account.

2. This is probably due to from “continuous” function to 

“step” function.

3. Impose some threshold. 

Design 3-D targets

different targets for different devices ?
II. Relative Measurement

This is extremely important in terms of interoperability

between images captured by new devices and a huge 

amount of images now existing in our systems.

1. No flip-flop image: 

It is caused by the combination effects of the illumination

directions inside the device and the orientations of

ridges and valleys that are different between those at the 

upper part (generally vertical) and those at the lower part 

(mostly horizontal) of human’s fingers.

Absolutely no algorithm can effectively and efficiently 

extract minutiae from such kind of fingerprint image.

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All images obtained using live scanners are mirror 

images.

2. Mirror image as opposed to front-view image

3. Bimodal distribution of grayscales of all 

pixels in an ROI

4. The issue of the variance from software tools

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5. Speed

The real time of one transaction = the time of

positioning fingers into the right position (the issue 

of usability; in reality most are the 1st time users) + 

the capture time executed by the system (hardware & 

software)

6. A primitive test

Using the same subject, 1) get an image from a

certified live scanner, 2) get an image from your

device, 3) then compare in terms of minutiae using 

eyes and/or software.

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1. Establish an “Evaluation” Steering Committee

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Please make recommendations!

Planning 

The Committee will consist of about five to ten members, 

and will have meetings from time to time until “the 

mission has been accomplished”.

2. Hold a workshop, if we have sufficient number 

of attendees.

3. Stipulate specifications.

4. Design targets to implement spec.