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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.

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biometrics with topics in face recognition

Biometrics with Topics in Face Recognition

Dr. Karl Ricanek, Jr.

Assistant Professor

Computer Science Dept

University of North Carolina, Wilmington

discussion overview
Discussion Overview
  • Biometrics
    • Definition/History
    • Technologies
  • Face Recognition
    • History/Issues
    • Research Focus
  • Questions and Answers
biometrics definition
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
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 history1
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 history2
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
Biometric Technologies: Selected
  • Fingerprint
  • Voice
  • Iris/retina
  • Gait
  • Face Recognition
biometric technologies
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 technologies1
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 technologies2
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 technologies3
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
Biometric Technologies: Face Recognition
  • History

Kanade 1977,

Kaya 1972,

Bledsoe 1964

Feature Metric

Akamtsu 1991

Brunelli 1992

Neural Network

Ricanek 1999

Variable Lateral

Pose Recognition

Turk 1991

Hong 1991

Shirovich 1987

Statistical

1888 Galton

Profile Id

Ricanek, Patterson & Albert 200X

Craniofacial Morphology:

Models for Face Aging

(Research in progress)

Psychophysic

neuroscience

approaches

face recognition techniques
Face Recognition Techniques
  • Image Based
    • Statistical based on O(2nd)
      • 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
FRT Diagram

Preprocessing

Preprocessing

Face Recognition

System

Probe

Gallery (DB)

Rank ordered lists

from gallery set with

confidence factor

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: Field Reports
face recognition technologies problems
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
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 progression1
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.
face recognition rates offline
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
team s research
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.
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