Biometrics for Personal

spectacularscarecrowAI and Robotics

Nov 17, 2013 (3 years and 4 months ago)

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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