Biometrics for Personal

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