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: Selected

FingerprintVoiceIris/retinaGaitFace 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 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.

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