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Face Recognition , Biometrices
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Biometrics with Topics in Face Recognition
Dr. Karl Ricanek, Jr.Assistant Professor
Computer Science DeptUniversity 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
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 GaltonProfile Id
Kanade 1977, Kaya 1972,Bledsoe 1964Feature Metric
Turk 1991Hong 1991Shirovich 1987Statistical
Akamtsu 1991Brunelli 1992Neural Network
Psychophysicneuroscienceapproaches
Ricanek 1999Variable LateralPose Recognition
Ricanek, Patterson & Albert 200XCraniofacial Morphology: Models for Face Aging(Research in progress)
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
Probe
Gallery (D
B)
Face RecognitionSystem
Rank ordered listsfrom 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 ProgressionMaturation 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)
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
0 5 10 15 20 25 30 35 40 45 500
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0.3
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0.5
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0.8
0.9
1
Face Recognition Rank Curve: Age Progression
0 5 10 15 20 25 30 35 40 45 500
0.1
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0.5
0.6
0.7
0.8
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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