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Fingerprint on the Move (FoM)™ Identification Framework for Identification on the Move
Ben Bavarian,
Frank Lou, and
Mehrad Tavakoli
Principal Consultants
AFIS and Biometrics Consulting Inc.
The research in this presentation is partly supported by the
DHS science and technology Contract No. NBCHC090025
2011© AFIS and Biometrics Consulting Inc
96th IAI Educational Conference
Milwaukee, Wisconsin
August 11, 2011
Lecture outline
2
Background / the question
Stand –still pose capture and processing
Moving hand freeze capture and processing
The laboratory prototype operation;
Video clips of how the Fingerprint on the Move
(FoM) ™ works.
Background / the Question
3
Build a prototype system for identifying subjects on the
stand-off and on the move;
Use COTS Sensors/Cameras; No special hardware.
The Question:
Is it possible to take a picture of the palmar area of the
hand and get the friction ridge details from the finger and
palm area and be used for identification and verification of
the individual reliably?
The COTS components used in the proejct
4
Digital Camera Back - A device that attaches to the back of the
camera, allowing larger sensor size to be used on the medium
format. We have used the Digital Transition Phase 1 digital camera
back with effective array size of 8964x6732 - 60mpixels, and
The Canon DSLR family of cameras – 5D Mark II; Rebel XOS and
Rebel T1; plus standard flash and LED lighting panels.
Can we make identification with non-contact hand scan processing
5
Sample Cropped and Pre-Processed Images
6
Can we make identification with non-contact hand scan processing – DSLR Camera set up
7
Hand scan processing steps–second set up
8
Image Binarization
Post-processing
Finger-palm segmentation
Pre-processing
Sample ink and livescan fingerprints from the background database
9
Sample Images with Their Corresponding Gray-Scale, Binarized and Skeletonized Images
10
Right Thumb Finger
11
Can we make identification with non-contact hand scan processing
12
Search Id: 4029f2 – Case 1, Right hand index finger
Search Print Number Minutiae: 123
Rank ID Candidate # of
Minutiae
Match Score
1 4030f2 123 262.3000
2 4029f5 98 0.0971
3 4030f5 98 0.0968
4 4029f4 82 0.0937
5 4030f4 97 0.0853
6 4029f3 122 0.0823
7 4030f3 133 0.0739
Fingerprint match report
Top 7 respondents
Segment the fingerprints
– obtained 750 ppi image
Interpolate to 500 ppi
Map to gray scale image
Use standard processing
to detect the ridge flow
and extract the minutiae
Summary results for fingerprint match score analysis Histogram of matching scores and non-matching scores, Flat captures, without thumb
13
Summary results for fingerprint match score analysis Histogram of matching scores and non-matching scores, Rolled captures, without thumb
14
Summary results for fingerprint match score analysis Histogram of the Flat and Rolled fingerprint match scores
15
Summary results for fingerprint match score analysis Sample match report for right index
16
Search Token: ID=1, Data type=Right Index, Session F
Data Type Subject ID Score
Flat-Right-Index 1 125
Rolled-Right-Index 1 65
Flat-Right-Little 53 17
Flat-Right-Little 1 17
Flat-Right-Little 35 15
Flat-Right-Middle 1 15
Flat-Right-Index 28 14
Flat-Right-Ring 35 14
Flat-Left-Little 49 14
Flat-Right-Ring 53 14
ROC plots True match vs. false match rate analysis for fingerprint match score ROC diagram for individual digits of fingers for image-based versus standard fingerprints
17
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False Acceptance Rate
Image vs FP Little Image vs FP Index Image vs FP Thumb
Image vs FP Middle Image vs FP Ring
ROC plots True match vs. false match rate analysis for fingerprint match score Quality based categorized ROC diagrams
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False Acceptance Rate
Good Quality Best Quality Worst Quality
Bad Quality Average Quality
ROC plots True match vs. false match rate analysis for fingerprint match score ROC diagram for best quality image-based fingerprints and standard livescan fingerprints
19
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False Acceptance Rate
FP vs FP Best Quality Image vs FP
Next generation of identification and authentication using the non-
contact scan of the palmar area of the hand with high resolution
COTS camera and automatically getting the information needed (
fingerprints and palmprint minutiae templates) to carry out the one to
one verification and one to many identification task.
The concept is like the barcode scanners on the check-out counters
in the supermarket. The surface of your hand is your unique
"barcode" and our protoyype set up will do the capture and process
and match automatically.
Fingerprint on the Move (FoM)
20
Stand off access control without using any fingerprint readers on the
access door. The product uses very high resolution digital back
camera to capture the hand image from 4 to 6 feet away process and
authenticate to provide the access for the person.
Follow up prototype concept of operation Hand-Match IDentification ™ (HaMID)
21
Sample Experimentation Fingerprint on the Move (FoM)
22
Two video clips demonstrating the FoM prototype operation
FoM images sample image and the output of the automatic segmentation from the hand picture.
23
Discussions / Q&A
The feasibility of processing hand palmar area photos to
carry out friction ridge detail feature extraction is
demonstrated;
The feasibility of freeze-capture of the moving hand is
demonstrated;
Possible future use for “your-fingerprint-your-unique-
barcode” for verification and identification on the move. 24
Dr. Bavarian is one of the pioneers and industry leading authority in the field of Biometric
Identification with over 26 years of R&D and management experience in industry and academics.
He founded the AFIS and Biometrics Consulting Inc. in 2007, providing subject matter expert
consulting and strategic management services in Biometrics Identification Industry. The company
has successfully completed more than two dozen contracts in the last three years.
Prior to ABC Inc. Dr. Bavarian was the Vice President of Motorola Biometrics, where he led the
business turn around and four fold increase in Sales by directing the development of the industry
leading Automated Biometric Identification System products with over 100 large scale
deployments.
Before moving to the industry in 1992, Dr. Bavarian was a professor in the Department of
Electrical and Computer Engineering at the University of California, Irvine, where he conducted
original research in image processing, computer vision, intelligent systems and published over
120 technical papers and received several awards for outstanding research and distinguished
teaching.
Dr. Behnam (Ben) Bavarian received his Ph.D. in Electrical and Computer Engineering from The
Ohio State University, Columbus Ohio in 1984.
Author Biography - Dr. Ben Bavarian
25
Dr. Lou is the Director of Research and Product Development at AFIS and Biometrics
Consulting Inc. Prior to joining ABC Inc. Frank was a principal research engineer at Motorola
Biometrics Business Unit where he has helped to advance the core AFIS technology and get
the best performance in the NIST and Customer Benchmarks.
At ABC Inc. he has lead the architecture and core technology development of the Biometrics
Identification on the Move System™ (BIMS), a break thorough solution to incorporate stand-
off biometric capture and matching for positive identification funded by the DHS Science and
Technology.
Dr. Lou received his Ph.D. from University of Washington, Seattle in 2004 with expertise in
Image processing, pattern recognition and Signals and Systems. He has publishes over 30
technical papers, report, Patents and disclosures, and book chapter in the related areas.
Author Biography - Dr. Frank Lou
26
Mr. Mehrad Tavakoli is PhD graduate student at University of California, Irvine California (UCI),
where he is pursuing his research in advancing Biometrics Identification science and its
application for Law Enforcement, Security and Commercial applications.
Mr. Tavakoli is the lead research scientist for the UCI project on Biometrics Identification on the
Move System™ (BIMS) where he has developed new techniques for using high resolution
imaging and the practical applications to capture of face, iris and fingerprint and palmprint friction
ridge details.
In his prior experience Mehrad has designed and build a Geo-Spatial Imaging system similar to
Google Streets, where massive amount of image database where captured and managed for
instant access. He has also led the R&D efforts for new projects in IT and Communications areas
such as new ad-hoc routing protocols, MAC-layer protocols, and underwater communication
which included the underwater surveillance security systems and solutions.
Mr. Tavakoli received his Masters of Science Degree from UCI, specializing in Computer
Architecture, Image processing, Signal and Systems and completing his thesis on Stand-Off
Biometrics Identification.
Author Biography - Mr. Mehrad Tavakoli
27