1

Evaluation of 3-D Fingerprint 

Image Capture Devices

(9/12/07)

Dr. Jin Chu Wu 
2

Objectives:

I. Absolute Measurement:

1. Stipulate the specifications. 

2. Design targets to implement spec.

II. Relative Measurement - interoperability: 

Compare with those collected from live 

scanners in terms of both accuracy and 

speed. 

III. New Discoveries
3

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!! 

Spatial Frequency Response

CTF: Contrast (square wave) Transfer Function 

MTF: Modulation (sine wave) Transfer Function

Gray Level Uniformity

adjacent row, column uniformity 

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. 4
5

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.
6

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”.

Dynamic Range:

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 ?

...... 
7

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.
8

2. Mirror image as opposed to front-view image 

All images obtained using live scanners are mirror 

images.

3. Bimodal distribution of grayscales of all 

pixels in an ROI

4. The issue of the variance from software tools
9

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.
Planning 

1. Establish an “Evaluation” Steering Committee

The Committee will consist of about five to ten members, 

and will have meetings from time to time until “the 

mission has been accomplished”.

Please make recommendations!

2. Hold a workshop, if we have sufficient number 

of attendees.

3. Set up a website at the NIST server to activate 

and facilitate all kinds of networking activities, 

such as discussion, making recommendations 

and suggestions, announcing the status and

news, etc. 10