Biometrics for Personal
Verification/Identification
Chaur
-
Chin Chen
Department of Computer Science
National Tsing Hua University
E
-
mail:
cchen@cs.nthu.edu.tw
Tel/Fax: (03) 573
-
1078/572
-
3694
Outline
•
What is Biometrics?
•
Motivation by Evidence
Iris Image Pattern Analysis
Handwriting/Handprinting Verification
Personal Signature Verification
Hand Geometry Verification
Voice (Speech) Pattern Recognition
Face Image Recognition
Fingerprint Image Verification/Identification
Palmprint, Ear shape, Gesture, …
•
Fingerprint Classification and Verification
•
Opportunities and Challenges
What and Why is Biometrics?
•
What is Biometrics?
Biometrics
is the science and technology of interactively measuring and statistically analyzing
biological data, in particular, taken from live people.
•
Why Biometrics?
(1) The banking industry reports that false acceptance rate (FAR) at ATMs are as high as 30%,
which results in financial fraud of US$2.98 billion a year.
(2) In U.S., nearly half of all escapees from prisons leave through the front door , posing as
someone else.
(3) Roughly 4000 immigration inspectors at US ports
-
of
-
entry intercepted and denied admission to
almost 800,000 people. There is no estimate of those who may have gotton through illegally.
(4) Personal verification/identification becomes a more serious job after the WTC attack on
September 11, in the year 2001.
The evidence indicates that neither a PIN number nor a password is reliable.
Some Biometric Images
Iris Image Pattern Analysis
•
The
iris
is the portion of texture regions surrounding the pupil of an eyeball.
•
The iris image can be sensed by a CCD camera under a regular lighting
environment.
•
An ancient French criminologist Berthillon did exploratory work linking iris
pattern to prisoner identity.
•
In 1980’s, opthamologists Leonard Flom and Aran safar posited that no two
irises were alike.
•
In 1994, Professor John Dougman develop algorithms using 2D Gabor
filters according to Flom and Safar’s concept to extract iris features for the
use in human authentication.
•
IrisCode, the feature vector of an iris, consisting of 512 bytes is recorded
and stored in the database for future recognition/matching. It takes less than
2 seconds in a Pentium III machine to compute an IrisCode.
•
Potential applications for iris scanning biometrics are widespread and
installations have been undertaken in the financial sectors for CityBank
ATMs as well as in some international airport for passenger identification.
http://www.astrontech.pl/html/body_iridian_merged.html
Handwriting/Handprinting Verification
Personal Signature Verification
•
Handwritings
and
Signatures
are behavioral biometrics rather than anatomical
biometrics such as an iris pattern or a fingerprint.
•
People handwrite digits or their names in their own special manners. An ancient
Chinese calligrapher Wang, Xizhi (AD 306~365) produced many beautiful writings
such that his signature would be paid for in gold.
•
Based on the
mechanics
of how we write is something very personal and often quite
distinctive, biometrics handwriting and/or signature seeks to analyze the dynamics
inherent in writing the digits, characters, letters, words, and sentences.
•
The features include how a person presses on the writing surface, how long a person
takes to sign his name, how a person struggles to maintain verticality, angularity in
letter forms and along the baseline, plus narrow letters.
•
http://www.handwriting.org/main/hwamain.html
•
Biometrics is the science and technology of interactively measuring and statistically
analyzing biological data, in particular, taken from live people
Hand Geometry Verification
•
Hand geometry systems
work by taking a 3D view of the hand in order to
determine the geometric shape and metrics around finger length, height,
and/or other details.
•
A leading hand geometry device measures and computes around 90
parameters and stores in a record of 9 bytes, providing for flexibility and
storage transmission.
http://cse.msu.edu/rgroups
Voice (Speech) Pattern Recognition
•
The basis for
voice
or
speech
technology was pioneered by Texas Instruments in the 1960’s.
•
The current voice recognition uses a standard microphone to record an individual’s voice and
identity its unique characteristics. It attempts to analyze the physiological characteristics that
produce speech, and not the sound or pronunciation.
•
A voice identification system requires that a “voice reference template” be constructed so that it
can be compared against subsequent voice identification. Voice identification systems incorporate
several variables or parameters in the recognition of one’s voice/speech pattern including pitch,
dynamics, and waveforms.
•
It is estimated that the revenues from voice/speech identification systems and telephony
equipments and services sold in America will increase from US$356 million in 1997 to US$22.6
billion in 2003.
•
Hidden Markov Model and Autoregressive Model
•
First Fourier Transform and Wavelet Analysis
http://www.buytel.com
Outline For Image Processing
•
A Digital Image Processing System
•
Image Representation and Formats
1. Sensing, Sampling, Quantization
2. Gray level and Color Images
3. Raw, RGB, Tiff, BMP, JPG, GIF, (JP2)
•
Image Transform and Filtering
•
Histogram, Enhancement and Restoration
•
Segmentation, Edge Detection, Thinning
•
Image Data Compression
R.C. Gonzalez and R.E. Woods, Digital Image Processing,
Prentice
-
Hall, 2002
Digital Image Analysis System
•
A 2D image is nothing but a mapping from a region to a matrix
•
A Digital Image Processing System consists of
1. Acquisition
–
scanners, digital camera, ultrasound, X
-
ray, MRI,
PMT
2. Storage
–
HD, CD (700MB), DVD (4.7GB), HD
-
DVD (20GB),
Flash memory (256 MB +)
3. Processing Unit
–
PC, Workstation, PC
-
cluster
4. Communication
–
telephone, cable, wireless
5. Display
–
LCD monitor, laser printer, laser
-
jet printer
Image Processing System
Gray Level and
C
o
l
o
r
Images
Pixel
s
in a Gray Level Image
Gray and Color Image Data
•
0, 64, 144, 196,
225, 169, 100, 36
(
R
,
G
,
B
) for a
c
o
l
o
r
pixel
Red
–
(255, 0, 0)
Green
–
( 0, 255, 0)
Blue
–
( 0, 0, 255)
Cyan
–
( 0,255, 255)
Magenta
–
(255, 0, 255)
Yellow
–
(255, 255, 0)
Gray
–
(128, 128, 128)
Image Representation
(Gray/
C
o
l
o
r
)
•
A gray level image is usually represented by an
M by N matrix whose elements are all integers in
{0,1, …, 255} corresponding to brightness scales
•
A color image is usually represented by 3 M x N
matrices whose elements are all integers in
{0,1, …, 255} corresponding to 3 primary
primitives of colors such as
Red
,
Green
,
Blue
Sensing
,
Sampling
,
Quantization
•
A 2D digital image is formed by a
sensor
which maps a region to a matrix
•
Digitization of the spatial coordinates (
x,y
)
in an image function
f(x,y)
is called
Sampling
•
Digitization of the amplitude of an image
function
f(x,y)
is called
Quantization
Gray Level and Color Images
Some Image File Formats
•
Raw
–
Raw image format uses a 8
-
bit unsigned character to store a pixel value of
0~255 for a Raster
-
scanned gray image without compression. An R by C raw image
occupies R*C bytes or 8RC bits of storage space
•
TIFF
–
Tagged Image File Format from Aldus and Microsoft was designed for
importing image into desktop publishing programs and quickly became accepted by a
variety of software developers as a standard. Its built
-
in flexibility is both a blessing
and a curse, because it can be customized in a variety of ways to fit a programmer’s
needs. However, the flexibility of the format resulted in many versions of TIFF, some
of which are so different that they are incompatible with each other
•
JPEG
–
Joint Photographic Experts Group format is the most popular lossy method
of compression, and the current standard whose file name ends with “.jpg” which
allows Raster
-
based 8
-
bit grayscale or 24
-
bit color images with the compression ratio
more than 16:1 and preserves the fidelity of the reconstructed image
•
EPS
–
Encapsulated PostScript language format from Adulus Systems uses Metafile
of 1~24
-
bit colors with compression
•
JPEG 2000
Image and Its Histogram
0
50
100
150
200
250
0
2
4
6
8
10
12
Histogram of Image Lenna
Edge Detection
-
1
-
2
-
1
0 0 0
X
1 2 1
-
1 0 1
-
2 0 2
Y
-
1 0 1
Large (|X|+|Y|)
Edge
Thinning and Contour Tracing
•
Thinning
is to find the skeleton of an image
which was commonly used for Optical Character
Recognition (OCR) and Fingerprint matching
•
Contour tracing
is usually used to locate the
boundaries of an image which can be used in
feature extraction for shape discrimination
Image
Edge, Skeleton,
Contour
Image Data Compression
•
The purpose is to save storage space and to
reduce the transmission time of information.
Note that it requires 6 mega bits to store a 24
-
bit
color image of size 512 by 512. It takes 6
seconds to download such an image via an
ADSL (Asymmetric Digital Subscriber Line) with
the rate 1 mega bits per second and more than
12 seconds to upload the same image
•
Note that 1 byte = 8 bits, 3 bytes = 24 bits
Lenna Image vs. Compressed
Lenna
Face Image Recognition
•
Face
recognition technology works well with most of the shelf PC cameras,
generally requiring 320*240 resolution at 3~5 frames per second.
•
Facial recognition software products range in price from US$50 to over
US$1000, making one of the cheaper biometric technologies.
•
Four primary methods used to identify to verify users by means of facial
features, including
eigenfaces, discriminant analysis, neural network
, and
ad hoc methods.
•
Singular Value Decomposition
and
Pattern Recognition
.
•
Fast Fourier Transform
and
Wavelet Analysis
http://facial
-
scan.com/facial
-
scan_technology.htm
http://www
-
white.media.mit.edu/vismod/demos/facerec
A Face Recognition Flowchart
Begin Image
Capture
Image
Preprocessing
Feature
Extraction
Classification
Decision
Person
1
Person N
Unknown
Persons
……
Face recognition flowchart
Face Database
•
YALE
•
P. N. Bellhumer, J. Hespanha, and D. Kriegman. Eigenfaces vs. fisherfaces: Recognition using
class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence,
Special Issue on Face Recognition, 17(7):711
--
720, 1997.
•
YALE B
•
Georghiades, A.S. and Belhumeur, P.N. and Kriegman, D.J. From Few to Many: Illumination
Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. Pattern Anal.
Mach. Intelligence 23(6):643
-
660 (2001).
•
ORL
•
Ferdinando Samaria, Andy Harter. Parameterisation of a Stochastic Model for Human
Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer
Vision, Sarasota FL, December 1994
•
AR
•
A.M. Martinez and R. Benavente. The AR Face Database. CVC Technical Report #24, June 1998
Fingerprint Image
Verification/Identification
•
Each
fingerprint
is a map of ridges and valleys in the epidermis layer of the skin.
•
The ridge and valley structures from unique geometric patterns.
•
A
minutiae
pattern consisting of ridge endings and bifurcations is unique to each
fingerprint.
•
Most of the contemporary automated fingerprint identification and verification systems
(AFIS) are minutiae pattern matching systems.
•
A modern AFIS is composed of 5 primary modules: (1) Image Enhancement, (2)
Image segmentation and Thining, (3) Minutiae Points Extraction, (4) Core and Delta
Localization, and (5) Point Pattern Matching.
•
A fingerprint forum [?] provided 5 sets of small databases for researchers to evaluate
their identification/verification software.
•
SecuGen EyeD and Veridicom are two leading companies selling both commercial
fingerprint identification/verification systems and sensors with resolution 500dpi.
Veridicom FPS110 fingerprint reader sensed a 300*300 fingerprint image in a 2cm by
2cm area.
•
http://www.networkusa.org/fingerprint.shtml
•
http://bias.csr.unibo.it/fvc2000
•
http://www.fpusa.com
FINGERPRINTS.DEMON.NL
A Paradigm for Fingerprint Matching
Thank You
•
Koala
Angel
wishes
you have a wonderful
university life
•
I am from Brisbane,
Australia and sleep
16 hours each day
but
you should not
•
May 10, 2005
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