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Fundamentals of Biometrics for Personal Verification/Identification


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Title: Fundamentals of Biometrics for Personal Verification/Identification

Fundamentals of Biometrics for Personal
  • Chaur-Chin Chen
  • Department of Computer Science
  • Institute of Information Systems and Applications
  • National Tsing Hua University
  • E-mail
  • Tel/Fax (03) 573-1078/ (03) 572-3694

  • What is Biometrics?
  • Motivation by Evidence
  • Iris Image Pattern
  • Handwriting/Handprint
    ing Verification
  • Personal Signature
  • Hand Geometry
  • Voice (Speech)
    Pattern Recognition
  • Face Image
  • Fingerprint Image
  • 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 US2.98
    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
  • (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
  • In 1980s, ophthamologists 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 Safars 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//

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 306365) 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//
  • 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//

Voice (Speech) Pattern Recognition
  • The basis for voice or speech technology was
    pioneered by Texas Instruments in the 1960s.
  • The current voice recognition uses a standard
    microphone to record an individuals voice and
    identity its unique characteristics. It attempts
    to analyze the physiological characteristics that
    produce speech, and not the sound or
  • 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 ones 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 US356 million in 1997 to US22.6
    billion in 2003.
  • Hidden Markov Model and Autoregressive Model
  • Fast Fourier Transform and Wavelet Analysis
  • http//

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,
  • X-ray, MRI, PMT
  • 2. Storage HD (40GB), CD (700MB), DVD
  • HD-DVD (20GB), Flash memory (256 MB )
  • 3. Processing Unit PC, Workstation,
  • 4. Communication telephone, cable, wireless
  • 5. Display LCD monitor, laser printer,
    laser-jet printer

Image Processing System
Gray Level and Color Images
Pixels in a Gray Level Image
Gray and Color Image Data
  • 0, 64, 144, 196,
  • 225, 169, 100, 36
  • (R, G, B) for a color 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/Color)
  • 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 0255 for a
    Raster-scanned gray image without compression. An
    R by C raw image occupies RC 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
    programmers 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 161 and preserves the fidelity of the
    reconstructed image
  • EPS Encapsulated PostScript language format
    from Adulus Systems uses Metafile of 124-bit
    colors with compression
  • JPEG 2000

Image and Its Histogram
Edge Detection
  • -1 -2 -1
  • 0 0 0 ? X
  • 1 2 1
  • -1 0 1
  • -2 0 2 ? Y
  • -1 0 1
  • Large (XY) ? 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
    320240 resolution at 35 frames per second.
  • Facial recognition software products range in
    price from US50 to over US1000, making one of
    the cheaper biometric technologies.
  • Four primary methods used to identify or verify
    users by means of facial features, including
    eigenface, PCA, 2D-PCA, LDA, 2D-LDA, wavelet
    analysis, neural network, and ad hoc methods.
  • Singular Value Decomposition and Pattern
  • Fast Fourier Transform and Wavelet Analysis
  • http//
  • http//

A Face Recognition Flowchart
Face Database
  • YALE
  • P. N. Belhumer, 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

Faces From The Same Person
Cumulative Distributions of Same Faces
Faces from Different Persons
Cumulative Distributions of Different Faces
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
    Thinning, (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 300300 fingerprint
    image in a 2cm by 2cm area.
  • http//
  • http//
  • http//
  • http//

FVC 2004
FVC 2004

A Paradigm for Fingerprint Matching
Fingerprints and Their Histograms
Fingerprint Image Processing
(No Transcript)
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
  • April 12, 2007

Are They From the Same Person?
Are They the Same Person?