Introduction to CBIRF
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EMBA 2009 – Information Systems and Applications Lecture III PowerPoint PPT Presentation


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Introduction to CBIRF and Biometrics Frank Yeong-Sung Lin Department of Information Management National Taiwan University. EMBA 2009 – Information Systems and Applications Lecture III. Outline. Introduction to CBIRF Introduction to (face-based) biometrics Discussions. 2.

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EMBA 2009 – Information Systems and Applications Lecture III

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Emba 2009 information systems and applications lecture iii

Introduction to CBIRF and BiometricsFrank Yeong-Sung LinDepartment of Information ManagementNational Taiwan University

EMBA 2009 – Information Systems and Applications

Lecture III


Outline

Outline

  • Introduction to CBIRF

  • Introduction to (face-based) biometrics

  • Discussions

2


Introduction to cbirf

Introduction to CBIRF

  • CBIRF – Content Based Image/Information Retrieval and Filtering

  • Characteristics

    • Adoption of only color, texture, shape and object position/size/orientation information in an image

    • No metadata or human indexing/annotation required

    • Real-time response

    • High scalability

    • High availability/reliability

    • Internet as the search target

    • Relevance feedback (learning)

  • Other applications

    • Anti-pornography Engine

    • Anti-leakage Engine (for protection of confidential images)

3


Introduction to cbirf cont d

Introduction to CBIRF (cont’d)

Content based image retrieval

Characteristics

Feature extraction

High dimensional indexing

Relevance feedback (learning)

Image

Features (color, texture, shape…)

Database

4


Introduction to cbirf cont d1

Introduction to CBIRF (cont’d)

Search Results

Query/Seed Image

5


Applications of cbirf

Applications of CBIRF

Image search

Video search

Logo search

IPR protection

Confidential image management

Objectionable image management

Image and photo organizer

Biometrics

6


Extension of cbirf porn filtering

Extension of CBIRF— Porn Filtering

Anti pornography engine

Applications

Email filtering

Desktop content management

Porn blacklist collection

Objectionable URL/Web content blocking

Pornography

Pornographic Features

Database

7


Extension of cbirf leakage detection

Extension of CBIRF— Leakage Detection

Anti (confidentiality/privacy) leakage engine

Applications

Email filtering

Confidential content management

Features

Database

Confidential Images

8


Introduction to biometrics

Introduction to Biometrics

Total biometrics industry revenue would grow from more than US$3.4 billion in 2009 to more than US$9.3 billion in 2014 (excluding the revenue from related professional and integration services).

(International Biometric Group, 2009-2014)

9


Emba 2009 information systems and applications lecture iii

Introduction to Biometrics (cont’d)

TLJ Confidential

10


Emba 2009 information systems and applications lecture iii

Introduction to Biometrics (cont’d)

  • Remarks by Bill Gates, Chairman and Chief Software Architect, Microsoft CorporationIT Forum 2004Copenhagen, Denmark, November 16, 2004

    • Passwords will soon be a thing of the past, replaced by biometric and smart-card technology, Bill Gates reiterated on Tuesday. – from Tech News on ZDNews

    • Another major issue for identity systems is, of course, the weakness of the password. Passwords have been the primary way that people identify who they are. Unfortunately, for the type of critical information on these systems and the regulations that ask that these systems be secure, whether it is health data, financial data or customer access to customer records where only certain people should have that information, we are not going to be able to simply rely on passwords. Therefore, moving to biometric identification and particularly in moving to smart cards, is a way that is coming. This is something that has been talked about for several years, but now we finally see the leading edge customers taking that step.

  • From “(i) what you have” to “(ii) what you know about” and eventually to “ (iii) who you really are”

  • ICAO advocates biometrics technologies, particularly face-based, for passport holder authentication.

11


Introduction to biometrics cont d

Introduction to Biometrics (cont’d)

Biometric Types Defined by ICAO (International Civil Aviation Organization)

First choice

12


Introduction to biometrics cont d1

Photo Taking

Face Detection

Facial Area Positioning

Facial Feature Extraction from the Facial Area

Facial Feature Archiving into the Specified Storage Device as a “Gallery”

Introduction to Biometrics (cont’d)

The Enrollment Process

13


Introduction to biometrics cont d2

Photo Taking

Face Detection

Facial Area Positioning

Facial Feature Extraction from the Facial Area

Retrieval of the Enrolled Facial Feature (Gallery) from the Storage Device

ACCEPT

Intelligent Comparison of the 2 Feature Sets

Comparison Result Reporting

Introduction to Biometrics (cont’d)

The Facial Feature Verification Process

14


Introduction to biometrics cont d3

Introduction to Biometrics (cont’d)

  • Advantages of face-based over fingerprint-based biometric approaches

    • More convenient

    • Less intrusive

    • More hygienic

    • Leveraging on existing infrastructure (webcam)

    • Less prone to duplicate (fingerprints easily available on protected devices, e.g. NBs)

    • Capable of continuous verification

    • Verifiability by human eyes

    • Effects of deterrence and non-repudiation by logging probe/novel images

15


Emba 2009 information systems and applications lecture iii

Introduction to Biometrics (cont’d)

  • Characteristics of desirable face verification technologies

    • Suitability for PCs/NBs/UMPCs/PDAs/Mobile Phones

    • Insensitivity to lighting, pose, expression and accessory

    • variations

    • Low enrollment time

    • Low verification time

    • User adjustable and personalized sensitivity

    • Dynamic thresholding

    • Intelligent and self-learning galleries

    • Factuality/Liveness detection

    • Extremely high accuracy: e.g. product of FAR (False Acceptance

    • Rate) & FRR (False Rejection Rate) lower than 10-6

    • Integration with other, e.g., the credential (ID and password)

    • mechanism

16


Merry christmas and happy new year

Merry Christmas and Happy New Year!

17


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