1. Introduction to CBIRF and Biometrics Frank Yeong-Sung Lin Department of Information Management National Taiwan University EMBA 2009 – Information Systems and Applications Lecture III

2. Outline Introduction to CBIRF Introduction to (face-based) biometrics Discussions 2

3. 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

4. 4 Introduction to CBIRF (cont’d) Content based image retrieval Characteristics Feature extraction High dimensional indexing Relevance feedback (learning) ImageFeatures (color, texture, shape…) Database

5. 5 Introduction to CBIRF (cont’d) Query/Seed Image Search Results

6. 6 Applications of CBIRF Image search Video search Logo search IPR protection Confidential image management Objectionable image management Image and photo organizer Biometrics

7. 7 Extension of CBIRF — Porn Filtering Anti pornography engine Applications 1.Email filtering 2.Desktop content management 3.Porn blacklist collection 4.Objectionable URL/Web content blocking PornographyPornographic FeaturesDatabase

8. 8 Extension of CBIRF — Leakage Detection Anti (confidentiality/privacy) leakage engine Applications 1.Email filtering 2.Confidential content management Confidential Images FeaturesDatabase

9. 9 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)

10. 10 Introduction to Biometrics (cont’d) TLJ Confidential

11. Remarks by Bill Gates, Chairman and Chief Software Architect, Microsoft Corporation IT Forum 2004 Copenhagen, 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. Introduction to Biometrics (cont’d) 11

12. 12 Introduction to Biometrics (cont’d) First choice Biometric Types Defined by ICAO (International Civil Aviation Organization)

13. Introduction to Biometrics (cont’d) The Enrollment Process Photo Taking Facial Area Positioning Face Detection Facial Feature Extraction from the Facial Area Facial Feature Archiving into the Specified Storage Device as a “Gallery” 13

14. Introduction to Biometrics (cont’d) The Facial Feature Verification Process Photo Taking Facial Area Positioning Face Detection Facial Feature Extraction from the Facial Area Retrieval of the Enrolled Facial Feature (Gallery) from the Storage Device Intelligent Comparison of the 2 Feature SetsComparison Result Reporting ACCEPT 14

15. 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

16. 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

17. Merry Christmas and Happy New Year! 17