face reg pres 1

8/9/2019 Face Reg Pres 1

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Face Recognition

Joshua I. Cohen


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Introduction to BiometricsThe average adult working in a large business has 12

passwords to remember, and spends nearly a week inevery year logging into systems.

The average cost to a large company for every

password lost is $16.


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Biometric Systems II

Vein Recognition


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Biometric Systems III

Face Recognition

Multiple Biometrics


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History ofFacial Recognition

Late 1980s: Research

Mid 1990s: Commercialization Current

- Authentication

- ID

- Law Enforcement


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History: Current Times I

September 24, 1999: OLETC ILEFIS

- 64 facial features

- 256 unique shapes / feature- quicker processing, look-up time

January 2001: Privacy Debate

- Super Bowl

- Tampa Entertainment District September 11, 2001: Impact on Market

- Visionics


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History: Current Times

II

The mood in this country has changed dramatically in just

seven days. Until last week we were trying to expand

peoples privacy against incursions from the government.

Now we might have to fight for what we already have.

-State Senator Ken Gordon, D-Denver, Chairman of the

-Senate Judicial Committee

September 21, 2001: Looking Ahead

- Colorado DMV: July 2001

- Neighborhoods (ie, Tampa)


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Facial Recognition

Market Outlook Physical Access Control

- 5 years

- casinos, immigrantsat border crossings

Computer UserAccess Control

No ones privacy is at stakeexcept for the privacy of

criminals and intruders.

House Majority Leader DickArmey, July 2001


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Advantages Over

Competing Systems Voluntary Action vs Passive Usage

Data Acquisition- 5% cannot provide good fingerprint

- environmental interference

Cost

- Iris Detection (movement)


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Theory Behind

Facial Recognition I Eigenface Technology:

Local Feature Analysis: 32-50 blocks


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Theory Behind

Facial Recognition II Identalink TrueFace


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Current Commercial

Products Visionics Corporation: Jersey City, NJ

- peaks and valleys

- 80 nodal points, 14-22 needed- golden triangle

- faceprint

Viisage Technology:Littleton, MA

- 128 archetypes on record- differences/similarities with models onrecord


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Eye Identification Using

Neural Networks 2 Neural Networks

- Finding the eyes

-Identifying the person

Small vs Large Window


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InfraredImages

andEigenfaces II

Threshold Euclidean Distance


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Scale-Space Approach

from Profiles I Profile Line

Locate NoseTip


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Scale-Space Approach

from Profiles II Locate Extrema (1, 2,

4-9) and InflectionPoints (10 12)

Feature Vectors

Euclidean Distance


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Morphological

Operations on Profiles I 2-D shape represented by 1-D function

Dilation Erosion


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Morphological

Operations on Profiles II 3 Shapes:A, M1, M2

3 feature vectors

- centroid face- centroid hair

Minimal Euclidean Distancebetween 2 profile images


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Project Selection / Outline Algorithm: MATLAB implementation of

face recognition profile matching

Database: MATLAB development of file

system Data Acquisition:Multimedia Lab video

camera or digital camera


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Timeline

10/22 11/26: Implement Algorithm

11/19:Mid-Project Presentation

11/26:Progress Report 1

11/26 12/10: Implement Database

12/3:Progress Report 2

12/10 12/17: Debug

12/10:

P

rogress Report 3 12/17:Final Project Presentation


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References

Ross Cutler, Face Recognition Using InfraredImages and Eigenfaces, April 1996.

Age Eide, Christer Jahren, Stig Jorgensen, ThomasLindblad, Clark S. Lindsey, and Kare Osterud, EyeIdentification for Face Recognition with NeuralNetowrks, 1996.

Zdravko Liposcak and Sven Loncaric, FaceRecognition from Profiles Using MorphologicalOperators, 1998.

Zdravko Liposcak and Sven Loncaric, A Scale-Space Approach to Face Recognition from Profiles,1999.

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