Biometrics with Topics in Face
Recognition
Dr. Karl Ricanek, Jr.
Assistant Professor
Computer Science Dept
University of North Carolina, Wilmington
Discussion Overview
Biometrics
Definition/History
Technologies
Face Recognition
History/Issues
Research Focus
Questions and Answers
Biometrics Definition
(Merriam
-
Webster online): the statistical analysis
of biological observations and phenomena.
Biometrics are automated methods of
recognizing a person based on a physiological
or behavioral characteristic.
(
http://www.biometrics.org
)
Phenotypic biometric
–
based upon features or
behaviors that are acquired through experience and
development.
Genotypic biometric
–
based upon genetic
characteristics or traits.
Biometrics History
First documented example: Egypt several
thousand years ago.
(Biometrics: Advanced Identity Verification
the complete guide, Julian Ashbourn)
Khasekem, assistant to chief administrator,
used phenotypic biometrics for identification of
food provisions.
Notes were kept on every worker (100,000 or more)
detailing physical characteristics (eg. age, height,
weight, deformities) and behavioral characteristics
(eg. General disposition, lisp/slurs in speech, etc.)
Biometrics History
Biblical Reference
Judges 12:5
-
6: “Then said the men of Gilead
unto him, Say now Shibboleth: and he said
Sibboleth: for he could not frame to pronounce
it right. Then they took him, and slew him at the
passages of the Jordan: and there fell at that
time of the Ephraimites forty and two thousand.”
Phenotypic biometric, in particular, voice, was
used to identify Ephraimites, the enemy of the
Gileadites.
Ephraimites pronounced “Sh” as “S”
Biometrics History
Modern
Belgian mathematician and astronomer Adolphe
Quetelet ushered in the modern use of biometrics with
his treatise of 1871, “
L’anthropometrie ou mesuare des
diffenretes facultes de l’homme
”
Frenchman Alphonse Bertillon, applied Quetelet work to
develop a system to identify criminals based on
anatomical measures.
Argentinean police officer Juan Vucetich was the first to
use dactyloscopy in 1888. Dactyloscopy is the taking of
fingerprints using ink.
Biometric Technologies: Selected
Fingerprint
Voice
Iris/retina
Gait
Face Recognition
Biometric Technologies
Fingerprint
Pros:
Years of research and
understanding
Security community
comfortable with technology
Innately distinctive feature
Cons:
Can be altered/worn over
time
Some ethnic groups exhibit
poor discrimination of finger
prints
Automatic techniques not
trusted
Biometric Technologies
Voice
Pros
Non
-
invasive
Distinctive w.r.t. vocal
chords, vocal tract,
patalte, sinuses, and
tissue w/in mouth
Cons
Easily corrupted with
noise
High false rates (positive
and negative) w.r.t.
physical ailments (colds,
sinus drains, etc.)
Biometric Technologies
Iris/Retina
Pros
Innately unique
No change over time
(static)
Left and right within
themselves
Genetic inheritance
(Genotypic)
Cons
Acquiring image
•
Alignment/position
•
Pupil size change
Biometric Technologies
Gait
Pros
Non
-
invasive
Discriminate under
various conditions (eg,
walking, jogging,
running)
Promising research
Cons
Can be altered
Too early in research
Biometric Technologies: Face
Recognition
History
1888 Galton
Profile Id
Kanade 1977,
Kaya 1972,
Bledsoe 1964
Feature Metric
Turk 1991
Hong 1991
Shirovich 1987
Statistical
Akamtsu 1991
Brunelli 1992
Neural Network
Psychophysic
neuroscience
approaches
Ricanek 1999
Variable Lateral
Pose Recognition
Ricanek, Patterson & Albert 200X
Craniofacial Morphology:
Models for Face Aging
(Research in progress)
Face Recognition Techniques
Image Based
Statistical based on O(2
nd
)
PCA/Eigenfaces (dominant)
Fisherfaces (LDA)
Etc.
Template matching
Spectral analysis
Gabor filtering
Etc.
Feature Based
Geometric
Feature metrics (spatial
relationships)
Morphable models
(shape/texture)
FRT Diagram
Probe
Gallery (DB)
Face Recognition
System
Rank ordered lists
from gallery set with
confidence factor
Preprocessing
Preprocessing
Face Recognition Technologies: Field
Reports
ACLU Press Release:
Data on
Face
-
Recognition Test at Palm
Beach Airport Further
Demonstrates Systems' Fatal
Flaws
. May 14, 2002.
ACLU press release:
Drawing
a blank: Tampa police records
reveal poor performance of
face
-
recognition technology:
Tampa officials have
suspended use of the system
.
Jan. 3, 2002.
Etc.
Reports that system in real
world app was effective 53% of
the time
“System logs obtained by the
ACLU through Florida's open
-
records law show that the
system never identified even a
single individual contained in
the department’s database of
photographs.”
Face Recognition Technologies:
Problems
Resolution/Quality
Orientation
Scale
Disguise
Lighting
Image Currency
Physiologic changes
due to growth
Physiologic changes
due to aging
My Research Niche: Age Progression
Age Progression
Growth
–
from infancy
to full maturation (~18)
Maturation
–
from full
maturation to
senescence (elderly
years)
My Research Niche: Age Progression
Maturation Age Progression
Face undergoes significant changes during the
adult age progression which dramatically
impacts face recognition technologies.
Loss of epidermis elasticity causes the formation of
rhytides and ptosis.
Elasticity loss is caused primarily by photoaging but
contributory factors include smoking, alcohol
consumption, drug use, and some prescribed
medications.
Skin texture changes occur also, rougher skin,
blotchiness/discoloration, hanging skin, etc.
My Research Niche: Age Progression
Face Recognition Rates (offline)
Probe
-
Gallery (temporally current)
Image based: mid 90%
Feature based: mid 90%
Probe
-
Gallery (temporally displaced)
Image based: 80% (1yr)
–
50% (5yr)
Feature based: unknown
Face Recognition Rank Curve: Normal
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Face Recognition Rank Curve: Age
Progression
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Team’s Research
Constructing the first craniofacial database where each subject
contains multiple images that span from late adolescences
through senescence.
Formulate understanding of the mechanisms of morphological
changes in the human face as it ages from late adolescence (i.e.,
ages 18
-
21 years) to senescence (i.e., ages 60+ years).
Which features fundamentally change with age?
Which features DO NOT change with age?
Develop models based on analysis of features for consistent
patterns versus idiosyncratic variations of craniofacial change due
to aging. Develop soft tissue texture map models that simulate
aging of skin.
Detailed evaluation of FRT against the database.
How and why does the FRT algorithm fail?
Develop FRT algorithm that is robust against aging.
Develop face detection and tracking techniques.
Questions and Answers
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