Transcript Document 7348435

Fingerprint Sensing
Techniques, Devices and
Applications
Rahul Singh
[email protected]
30th April 2003
1
Fingerprint Biometric
► First
used in China in 700 AD
► Proposed in Europe in 1858, implemented in
Germany in 1903.
► Unique – So far no two prints from different
fingers have been found that are identical
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Fingerprint Biometric Characteristics
► Fingerprint
is the representation of the
epidermis of a finger
► Set of (almost/often) parallel ridge lines
► Ridges produce local patterns
Source: http://www.biometrika.it/eng/wp_fingintro.html
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Fingerprint Biometric Characteristics
► Five
main classes of
fingerprints
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
Arch
Tented Arch
Left Loop
Right Loop
Whorl
Source: http://www.biometrika.it/eng/wp_fingintro.html
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Fingerprint Sensing
►
Two stages
1. Capture Fingerprint image
2. Process image and extract features
3. Store data for comparison or compare with
stored templates
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Types of Fingerprint Sensors
► Optic
Reflexive
 Finger lies on a prism. Total internal reflection produces
image of fingerprint on a camera chip
► Optic
Transmissive with Fiber Optic Plate
 Light source illuminates through the finger
 Finger lies on fiber-optic plate that transmits image data
to camera chip
► Optical
Line
 Pixel array measures the light reflected by the finger
Source: http://home.t-online.de/home/manfred.bromba/fpfaqe.htm
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Types of Fingerprint Sensors
► Capacitive
Line
 Capacitor array measures the capacitance at each pixel
► Thermal
Line
► Pressure
Sensitive
► Dynamic
Capacitive
 Finger is moved across a narrow array of thermal
sensors
 Temperature varies across the grooves and ridges
 Thermal sensors measure the temperature differences
over time
 Sensor measures the pressure per pixel
 Capacitance is measured by A/C voltage
Source: http://home.t-online.de/home/manfred.bromba/fpfaqe.htm
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Types of Fingerprint Sensors
► Static
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Capacitive Type 1
One electrode per pixel
Capacitance measured w.r.t neighboring pixel.
If pixel is on a groove capacitance is small
If pixel is on a ridge then capacitance is large
► Static
Capacitive Type 2
 Same as above except capacitance is measure w.r.t
ground
► Acoustic
(Ultrasound)
 Image of fingerprint is recorded by very high frequency
sound
Source: http://home.t-online.de/home/manfred.bromba/fpfaqe.htm
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Capacitive Sensing
► Fingerprint
consists of tightly spaced ridges and
valleys
► Sensor consists of a capacitive array
► Capacitive array acts as one plate of a capacitor
while the finger acts as the other
► Each pixel in the array is charged to a reference
voltage and allowed to discharge with a reference
current
► The rate of change of potential at each pixel is
proportional to the capacitance seen by the array
iref  C
dv
dt
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Capacitive Sensing
1.
2.
3.
4.
5.
Charge amp reset. Inverter O/P settles to threshold
Ref. charge applied to I/P
O/P Voltage proportional to feedback capacitance
Inverter O/P = upper saturation level if there is no feedback capacitance
Inverter O/P = close to logical threshold when feedback capacitance is large
Source: http://www-micro.deis.unibo.it/~tartagni/Finger/FingerSensor.html
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Capacitive Sensing
► 300
x 300 pixel array (90,000 pixels)
► 500 dpi Fingerprint image
Source: http://www.fme.fujitsu.com/products/biometric/pdf/Find_FPS.pdf
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Optical Sensing
►
►
►
Finger touches light emitting TactileSense polymer
Photodiode array embedded in the glass detects
illumination
Image is captured and transferred for storage
Source: [Tactilesense] http://www.ethentica.com/tactwhtpr.pdf
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Optical Sensing
► Sensing
by projecting an
image of the fingerprint
onto a camera by total
internal reflection.
Source: http://www.biometrika.it/eng/wp_fingintro.html
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Optical Vs Capacitive
► Capacitive
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Greater miniaturization
Newer technology
Can be embedded into small devices
Prone to dirt etc since finger touches silicon
Relatively cheap
► Optical
Sensors
 Larger sensing area since manufacturing large pure
silicon chips is expensive
 More robust. Longer life
 More expensive
 Better image quality and higher resolution
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Factors affecting the scan
► Image
quality
 Sharpness
 Contrast
 Distortion
Source: http://www.biometrika.it/eng/wp_scfing.html
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Factors affecting the scan
► Resolution
– higher is better
 Too low and we cannot detect
the minutiae
► Sensing
area
 Average fingerprint is about
0.5” x 0.7”
 Large area (1.0” x 1.0”)
ensures that overlap effects
(leading to false rejections)
are reduced
Source: http://www.biometrika.it/eng/wp_scfing.html
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Data Storage and Matching
► Minutiae
or Galton
Characteristics
 Termination of Ridge lines
 Bifurcation of Ridge lines
Source: http://www.biometrika.it/eng/wp_fingintro.html
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Data Storage and Matching
► Final
data size = 300 to 600 bytes
Source: http://www.fme.fujitsu.com/products/biometric/pdf/Find_FPS.pdf
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Data Storage and Matching
► Directional
Map
 Discrete matrix whose elements denote the orientation
of the tangent to ridge lines
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FX2000
►
FX2000 – Optical
Sensor
Efficiency
 Database of 100 users
(non-experts)
 Low quality fingerprints
TASK
SPEED_DEFAULT
SPEED_FAST
Feature extraction
350 ms
260 ms
Matching
120 ms
47 ms
Identity verification (1:1)
470 ms
307 ms
Identification (1:50)
3.35 sec
1.43 sec
Accuracy
SPEED_DEFAULT
Verification time (1:1)
SPEED_FAST
threshold t
FAR
FRR
FAR
FRR
0.3500
0.0049 (0.49%)
0.0005 (0.05%)
0.0032 (0.32%)
0.0009 (0.09%)
0.3750
0.0025 (0.25%)
0.0010 (0.10%)
0.0014 (0.14%)
0.0014 (0.14%)
0.4000
0.0011 (0.11%)
0.0014 (0.14%)
0.0005 (0.05%)
0.0020 (0.20%)
0.4250
0.0006 (0.06%)
0.0019 (0.19%)
0.0002 (0.02%)
0.0034 (0.34%)
0.4500
0.0004 (0.04%)
0.0026 (0.26%)
0.0000 (0.00%)
0.0049 (0.49%)
0.4750
0.0000 (0.00%)
0.0036 (0.36%)
0.0000 (0.00%)
0.0063 (0.63%)
Time to verify the identity
Identification time (1:50)
Average time to identify
an individual. 50
Users. Match is
found in the
middle.
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Secugen FDA01/FCA01
► Optical
sensor
► Resolution = 500 dpi
► Verification time = < 1
second
► Sensing area =
13.6mm x 16.2mm
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Authentec FingerLoc
► AF-S2
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Capacitive
68 pin PLCC
Resolution: 250 dpi
Array size: .512”x.512”
► AFS8500
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Capacitive
144 pin LQFP
Resolution: 250 dpi
Array Size: .384” x
.384”
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Biomouse/Biomouse plus
► Optical
sensor
► “High speed” matching algorithm – 400
prints per second on pII 400.
► Resolution = 500 dpi
► Average template size = 350 bytes
► Biomouse Plus comes with built in smart
card reader
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Defeating Fingerprint Scanners
► Gummi
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bears defeat fingerprint sensors
Japanese cryptographer
Gelatin + plastic mould
Latent fingerprints from glass
Cyanoacrylate Adhesive (superglue fules)
Digital camera
Adobe Photoshop
Photosensitive PCB – etched print in copper
Moulded finger with print
Source: http://www.theregister.co.uk/content/55/25300.html
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Defeating Fingerprint Sensors
► More
sophisticated devices use incorporate
biosensing modules prior to fingerprint
capture
► Detect blood flow
► Detect body heat
► Sensor shuts down if no life is detected
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Types of attack
► Brute
force
► Latent print
► Replay
► Trojan Horses
► Fake feature
► Dead feature
► Other (software leaks, bad security policies
etc)
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Applications
► Secure
logins via keyboard modules
► User identification at kiosks
► Biometric door locks
► Credit card security
► Weapon activation
► Theft protection
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Fingerprint Verification for Smart Cards
Motorola, Australia
► Senior
Honors thesis
► Develop biometric security solution
(prototype) for Motorola dual-slot phones
► Users insert credit card into slot 1 for ecommerce
► Smart card with embedded biometric into
slot 2
► Fingerprint sensor on phone identifies user
and authorizes use of credit card
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Fingerprint Verification for Smart Cards
Motorola, Australia
Enrollment
X.509 Certificate
Fingerprint template
1010011010100101
Smart Card
X.509 Certificate
Fingerprint template
1010011010100101
http://www.roma.unisa.edu.au/08216/99u/index.html
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Fingerprint Verification for Smart Cards
Motorola, Australia
Verification
Fingerprint template
1010011010100101
Compare
Smart Card
X.509 Certificate
X.509 Certificate
Fingerprint template
1010011010100101
Fingerprint template
Fingerprint template
1010011010100101
1010011010100101
http://www.roma.unisa.edu.au/08216/99u/index.html
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Questions
?
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