Fingerprint, seminar at IASRI, New Delhi
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Transcript of Fingerprint, seminar at IASRI, New Delhi
Security Systems using Fingerprint Biometry
Nishikant P. TaksandeIASRI, New Delhi, India
Contents Introduction Biometric Recognition Biometric Systems Fingerprints Fingerprint Recognition System Minutiae Extraction Minutiae Matching Applications Limitations Conclusions References
Introduction“Biometrics is the science of verifying
and establishing identity of an individual through physiological features or behavioral traits.”
Biometric is more about what you are than what you have or know
Password and tokens are not reliable
Introduction…Biometric has been used since 14th century in china
Everyone have unique physical or behavioral characteristics and so unique Identity
Enhanced convenience and augmented security measure
Biometric Recognition Biometric modalities
Physical Biometrics: Face, Fingerprints, Iris-scans, Hand
geometry
Behavioral Biometrics: Speech, Signature, and Keystroke dynamics
Chemical biometrics: Odor and the chemical composition of
human perspiration
Iris Iris is the area of the eye where the pigmented or coloured circle, usually brown, blue, rings the dark pupil of the eye
Image is typically captured using a noncontact imaging process
Iris should be at the predetermined distance from the focal plane of the camera
Iris portion
Face RecognitionMethod of acquiring face images is nonintrusive
Facial disguise is of concern in unattended recognition applications
It is challenging to develop techniques that can tolerate the effects of aging, facial expression, variations in the imaging environment
Hand and Finger Geometry
Features related to the human hand are relatively invariant and peculiar to an individual
System requires cooperation of the subject to capture frontal and side view images of the palm flatly placed on a panel with outstretched fingers
Only used for verification
Hand Vein Recognition
Back of a clenched fist used to determine hand vein structure
Systems for vein capture use inexpensive infra-red light emitting diodes
Voice Recognition
Voice capture is unobtrusive and only feasible applications requiring person recognition over a telephone
Voice signal available for recognition is typically degraded in quality by the microphone, communication channel
Voice is affected by factors such as a person’s health, stress and emotional state
SignatureThe way a person signs his name is known to be a characteristic of that individual
Signature is a behavioural biometric that changes over time
Professional forgers can reproduce signatures of others to fool the unskilled eye
Biometric Systems
An important issue in designing a practical biometric system is to determine how an individual is going to be recognized
Biometric systemEnrolmentVerification system Identification system
Enrolment
Feature Extraction
Template Creation
Data Storage
Template Identifier
SampleCapture Feature set
Subject Identifier
Enrolment Process
Verification
Feature Extraction Matching Data
Storage
Claimed Identity
SampleCapture Feature set
Subject Identifier
Verification Process
One Subjects template
Match or Non-Match
Identification
Feature Extraction
Pre-selectionand
MatchingData
Storage
SampleCapture Feature set
Subject Identifier
Identification Process
‘N’ Subject Templates
Subject Identified Or not Identified
Architecture
Biometric sensor Feature extraction
Database
Biometric sensor
Matching
Enrolment
Authentication Result
Feature extraction
General architecture of a biometric system
Comparison of Biometric Traits
Comparison of commonly used biometric traits
FingerprintSkin on human fingertips contains ridges and valleys which together forms distinctive patterns
These patterns are fully developed under pregnancy and are permanent throughout whole lifetime
No two persons have the same fingerprints
Automatization of the fingerprint recognition process turned out to be success in forensic applications
Fingerprint Patterns
Left Loop Right Loop
Fingerprint Patterns...
Whorl Arch Twin Loop
Fingerprint FeatureMinutiae Based Approach
Minutiae is the unique, measurable physical point at which ridge bifurcate or ends
Ridge Ending Ridge Bifurcation
Fingerprint Recognition System
Minutiae Extractor
Minutiae Matcher
Sensor
Architecture for Fingerprint Recognition System
Fingerprint Sensing and Storage
Off- line scan or live scan
A special kind of off-line images, extremely important in forensic applications, are the so-called latent fingerprints found at crime scenes
Live Scan Devices
Fingerprint image acquisition is the most critical step of an automated fingerprint authentication system
Idea behind each capture approach is to measure in some way the physical difference between ridges and valleys
Physical principles like capacitive, optical and thermal are used
Optical Sensors FTIR-based Fingerprint Sensor
Frustrated Total Internal Reflection (FTIR)
Minutia Extraction
Pre-Processing
Fingerprint Image Enhancement
Fingerprint Image enhancement is to make the image clearer for easy further operations
Increase the contrast between ridges and furrows and connect the broken points
Minutia Extraction...
Histogram Equalization
Histogram equalization is to expand the pixel value distribution of an image so as to increase the perceptional information
The Original Histogram of a fingerprint image
Histogram after histogram equalization
Minutia Extraction...
Histogram Equalization
Original Image Enhanced Image after Histogram Equalization
Minutia Extraction...
Fingerprint Image Binarization
8-bit Gray fingerprint image transform to a 1-bit image with 0-value for ridges and 1-value for furrows
Ridges in the fingerprint are highlighted with black colour while furrows are white
Minutia Extraction...
Fingerprint Image Segmentation
Region of Interest (ROI) is useful to be recognized for each fingerprint imageTwo step method is based on Morphological methods
ROI extraction by Morphological operations
Two Morphological operations called ‘OPEN’ and ‘CLOSE’ are adopted
Minutia Extraction...ROI extraction by Morphological operations
Original Image Area
ROI + BoundAfter OPEN operation
After CLOSE operation
Minutia Extraction...
Minutia Extraction
Fingerprint Ridge Thinning
Ridge Thinning is to eliminate the redundant pixels of ridges
Minutia Marking
If the central pixel is 1 and has exactly 3 one-value neighbours, then the central pixel is a ridge branch
If the central pixel is 1 and has only 1 one-value neighbour, then the central pixel is a ridge ending
Minutia Extraction...
Minutia Marking
Bifurcation Termination A fingerprint after minutiae extraction
Minutia Extraction...
Constellation Creation Technique
Minutia Extraction...
Constellation Creation Technique
Minutia Extraction...
Constellation Creation Technique
Minutia Extraction...
Constellation Creation Technique
The Matching Module
The matching module use a pre-processed pattern composed by the fingerprint, it’s minutia-file and it’s constellations,
These patterns are extracted from enrolled templates
Feature extraction is done then its patterns are compared to the reference ones
Comparison between the two fingerprints Constellation matchingMinutiae matching
Constellation Matching
When a new fingerprint is scanned it is passed by the feature extraction module, a set of constellations and their respective parameters are created
Comparison with the genuine constellations set extracted during system enrolment
F1 = {C1.0, C1.1, C1.2, C1.3} F2 = {C2.0, C2.1, C2.3, C2.4, C2.5}
Euclidean distance between the point patterns of constellations centres
Constellation Matching
Rejects a fingerprint template No constellation is matched D = 0A unique constellation that includes less than 15 minutiae is matched D = 0
Co1.0
Co1.1
Co1.2Co1.3
Co2.2
Co2.1
Co2.3
Co2.0
Fingerprint constellation matching
Minutiae Matching
Minutiae matching are performed within a constellation
The minutiae matching are used as a second level verification
minutiae matching algorithm proceeds as follow
Associate minutiae pointsCompute distance between minutiaeDecide
Minutiae MatchingFlow Chart
Start
Decision=reject
ConstellationMatching
Minutiae Matching
Decision=Accept
END
Fingerprint and constellation
files
CTH
MTH
D<CTH
D<MTH
NO
NO
Applications of Fingerprint Recognition System
Physical Access Control
Logical Access Control
Transaction Authentication
Device Access Control
Time and Attendance
Civil Identification
Forensic Identification
Limitations
Dirt , grime and wounds
Placement of finger
Too big database to process
Liveness important
In case of permanent finger injury
Barriers to Adoption
Business value is difficult to quantify in terms of return on investment
Fingerprint recognition systems, being an emerging technology, is sometimes confronted with unrealistic performance
The quality varies quite dramatically from one application to another and from one vendor to another
Several fingerprint system vendors are not financially stable
Conclusions
Most highly used methods for human recognition
Fingerprint system strongly relies on the precision
obtained in the minutia extraction process
Industrial commitment led to next generation
fingerprint technology
It has got broad acceptance from forensic to
handheld devices
Used for securing international borders
References
[1] FBI Fingerprint guide. Available online at
http://www.fbi.gov/hq/cjisd/takingfps.html
[2] Fingerprint. Available online at
http://en.wikipedia.org/wiki/Fingerprint
[3] Fingerprint recognition. Available online at
http://en.wikipedia.org/wiki/Fingerprint recognition
[4] Goyal, A., Study of Fingerprints Minutia Extraction and
matching Technique. Available online at
http://www.advancedcenterpunjabi.org/MTechP/Anjna
%20Goyal_M.Tech(CS).pdf
References...
[5] Human Fingerprints. Available online at
http://ww.fingerprinting.com/human-fingerprints.php
[6] Maltoni, D., Maio, D., Jain, A., Prabhakar, S.,(2009),
Handbook of Fingerprint Recognition, second edition,
Springer Limited.
[7] Murmu, N., Otti, A., Fingerprint recognition. Available online
at http://ethesis.nitrkl.ac.in/955/
[8] Rokbani, N., Alimi, A., Fingerprint identification using
minutiae constellation matching. Avialable online at
http://www.regim.org/publications/conferences/2005/2005_
MCCSIS_RA.pdf
THANK YOU