Research--3D Non-contact / Touchless Fingerprint
3D Non-contact / Touchless Fingerprints
 Data Acquisition and Processing of 3-D Fingerprints.
 Quality and Matching Performance Analysis of Three-dimensional Unraveled Fingerprints.
 Fit-sphere Unwrapping and Performance Analysis of 3D Fingerprints.
 Data Acquisition and Quality Analysis of 3-Dimensional Fingerprints.
Fingerprint recognition has been extensively applied in
both forensic law enforcement and security applications involving
personal identification. However, limitations are imposed upon the current fingerprint
capture technologies including:
1) obligatory maintenance of a clean sensor or prism
2) uncontrollability and non-uniformity of the finger pressure
on the device;
3) permanent or semi-permanent change of the finger
ridge structure due to injuries or heavy manual labors;
4) residues from the previous fingerprint capture;
5) data distortion under different illumination, environmental,
and finger skin conditions; and
6) extra scanning time and motion artifacts incurred in
technologies that require finger rolling.
In order to overcome these limitations, we have been developing a contactless / touchless 3D scanning system that employs structured light illumination (SLI). Our
ultimate goal is to simultaneously acquire 3D scans of all
the five fingers and the palm in high speed and fidelity using
multiple, commodity digital cameras and a DLP projector.
Post processing of these scans is then performed to virtually
extract the finger and palm surfaces, and create 2D flat
The advantages of our 3D fingerprint scanning
and processing technology include:
1) non-contact defuses distortion that exists in conventional
fingerprint acquisition system.
2) simultaneous acquisition of both texture and ridge
depth information of fingers;
3) automated fingerprint entry in no need of interaction
with the operator;
4) fast scanning (less than 0.8 second);
5) robustness to contamination of fingers and residues of
6) robustness to clutter and fraud because of the difficulties
in faking 3D fingerprints;
7) real-time feedback (less than 5 seconds) for users to
make position adjustment; and
8) low cost by using the off-the-shelf commodity camera
* The 3D contactless / touchless fingerprint scanner. The size of acquired image is 1420*2064 such that the ppi value can be as high as 1500.
* A 3D noncontact / touchless fingerprint scan. From left to right are: a 3D finger with texture (surface reflectance variations or albedo image); 3D fingerprint with depth rendering; cropped 3D fingerprint; and rotated crop.
3D Data Processing
To be compatible with current 2D automatic fingerprint identification system
(AFIS), we developed a new flattening algorithm for the 3D noncontact / touchless fingerprint which obtains both depth and albedo flattened fingerprints. The two fingerprints are further fused to obtain the final flatten result. After 3D fingerprint scans are unraveled into 2D flat equivalent fingerprints. The 2D flat equivalent fingerprints are further processed to be compliant with the National Institute of Standard and Technology (NIST) standards.
* Overlapping between 2D ink rolled fingerprints and flattened 3D fingerprints. 2D ink rolled fingerprints are binarized and in the left parts,
whereas the corresponding flattened 3D fingerprints are in the right parts. (a) Three combined images from 2D ink rolled fingerprint and depth flattened 3D
fingerprint. (b) Three combined images from 2D ink rolled fingerprint and albedo flattened 3D fingerprint.
* The fingerprint scan. From left to right are: a 2D plain fingerprint; the corresponding 3D scan; the 2D cropped unraveled equivalent fingerprint obtained from 3D scan.
The matching performances of depth, albedo and fused
fingerprint data sets are studied, using BOZORTH3 by NIST software package.
BOZORTH3 employs features to minutiae of the fingerprints
and produces a real valued similarity score. The higher the
score is, the more likelihood that the two fingers are from the
same finger of the same subject.
We note the output value as genuine score if the input
two fingerprints are actually from the same finger of the
same subject, whereas impostor score if the two fingerprints
are from the same finger but of different subjects.
* Distribution of genuine and impostor scores for depth, albedo and fused fingerprints. Result is obtained from 1011
genuine scores and 1011 impostor scores.
Based on the distributions, we give the ROC which
is a statement of the performance of the fingerprint verification
system. To evaluate the performance, we have to set
the operating threshold. For each threshold, we compute out
the False Accept Rate (FAR) and True Accept Rate (TAR).
For a generally specific FAR 0.01, the TAR of the fused data
set is 0.98, while the TAR of the depth and albedo are 0.93
and 0.96. All the three data sets achieve high matching performance, and the fused set outperforms the depth and albedo sets
* ROC of depth, albedo and fused flattened fingerprints.
For more details, please visit Flashscan3D,LLC or go to Yongchang's home.
Copyright © 2009 3D Imaging Laboratory, University of Kentucky. All rights reserved.