UCLA Extension

Biometric Identification: Theory, Algorithms, Performance and Practical Applications

Biometric identification technologies (the automatic recognition of individuals based on physical and/or behavioral characteristics) date back over 50 years to the earliest digital computers. Over the last two decades, biometric identification devices have become faster, cheaper, and more reliable, allowing for a variety of applications. This course looks at the history, theory, algorithms, applications, and standards of biometric recognition, including voice, iris, face, hand, and fingerprint identification.

Complete Details

  • To explain the current definition of “biometric recognition” and the distinctives of this form of “biometrics”
  • To list currently available technologies and how they work at the algorithmic level
  • To present a “system analysis” of the interaction of components in “real-world” applications.
  • To define performance metrics and their limitations in describing practical systems
  • To present independent test results and their value in performance prediction
  • To outline current international standards in the field.
  • To present several case studies of successful and unsuccessful applications of this technology, particular in large-scale environments.

Coordinator and Lecturer

James L. Wayman, PhD , has worked continuously in the field of automated human recognition since 1984. He is currently a contractor to the US, UK and AU governments, an IEEE Fellow and “Distinguished Lecturer”, a “core member” of the UK government’s “Biometric Working Group”, an IET Fellow, a Principal UK Expert (PUKE) on ISO/IEC JTC1 SC 37 international standards committee on biometrics, and the editor of both the ISO/IEC 19794-13 Voice Data Format and the ANSI/NIST Type-11 Voice Data Record. He is the Vice Chair of the US Organization of Scientific Area Committees (OSAC) subcommittee on Forensic and Investigative Speaker Recognition. He is co-editor of the Wayman, Jain, Maltoni and Maio, Biometric Systems: Technology, Design, and Performance Evaluation (Springer, London, 2005). He was director of the US National Biometric Test Center under the Clinton administration from 1997-2000 and was a member of the US National Academies of Science/National Research Council committees on “Authentication Technologies and their Implications for Privacy”, “Whither Biometrics” and Panel on Information Technology. He has been a paid biometrics advisor to 11 national governments.

Daily Schedule

Day 1

Introduction

  • A very short history of biometrics
  • Definitions
  • Core concepts
  • Taxonomy of applications
  • System description
  • Performance metrics

Day 2

Mathematical Underpinnings

  • Vector spaces
  • Distance measures and distributions
  • Correlation and covariance matrices
  • Eigen-Systems and principal component analysis
  • Sampling theory
  • Generalized Fourier Transform
  • One- and two-dimensional filtering
  • Neural nets and support vector machines
  • Probability fundamentals
  • The binomial distribution
  • Doddington’s Rule of 30 and the Rule of 3
  • Doddington’s Zoo
  • Duhem-Quine Hypothesis and holistic testing
  • Bayesian inversion of conditional probabilities (The elephant in the room)

Day 3

The Algorithms

  • Voice
    – Detection/Segmentation
    – Mel-scale cepstrum
    – GMM
    – HMM
    – iVectors and beyond
  • Face
    – Segmentation
    – Decomposition methods (eigenface, LFA, ICA)
    – Elastic Bunch Graphs
    – Local correlation techniques
    – ISO//IEC 19794-5
  • Fingerprint methods
    – Optical
    – Transform
    – Correlation
    – Minutiae extraction
    – Collection, transmission, and storage standards
    – ISO/IEC 19794-2
  • Iris
    – Daugman’s 1994 patent
    – Segmentation
    – Feature extraction
    – ISO/IEC 19794-6
  • Other Modalities
    – Hand geometry
    – Retinal scanning
    – Heartbeat

Day 4

Performance Testing and Applications International Testing Standards (iSO/IEC 19795)

  • NIST Test programs:
    – SRE
    – FpVTE
    – PTT
    – FRVT
    – MINEX
    – MBGC
    – MBE
    – IREX
    – Other NIST results
  • Case Studies
  • EasyPASS, SmartGate, and other border crossing systems
  • Social Service Systems
  • Vulnerability Assessment

Upon Program Completion

Participants will:

  • Understand how “biometrics” in the context of human identification differs from other forms of recognition technology
  • Be able to state precisely what functions these systems perform.
  • Be able to draw a system-level diagram for any biometric system and discuss its components.
  • Know which technologies are currently available, which are now defunct and how the computer algorithms analyze and compare patterns
  • Discuss performance metrics and their limitations in predicting “real-world” performance
  • Be able to reference on-line independent government test results
  • Be able to describe the state of international standards development
  • Name and describe several successful and unsuccessful government applications and some of the controversies surrounding them.

For more information contact the Short Course Program Office:
shortcourses@uclaextension.edu | (310) 825-3344 | fax (310) 206-2815

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