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CYB 5677 Biometric Authentication Technologies


Course Description

This course explores biometric authentication technologies including fingerprint, eye and facial biometrics. Presents biometric system design, applications, security considerations and other topics. Conveys key concepts through examples.

Course Objectives

Upon completion of this course, students should be able to

  • Examine and comparatively evaluate biometric systems
  • Evaluate the performance of biometric systems, including the application of ROC curves
  • Identify fingerprint recognition methods and compare the performance of fingerprint recognition systems
  • Compare face recognition methods and apply face detection and recognition methods
  • Explain the benefits and challenges of iris recognition systems
  • Compare the benefits and challenges of additional biometrics including ear, gait, hand geometry, and soft biometrics
  • Identify multibiometric system design considerations and fusion methods
  • Compare the security benefits and weaknesses of biometrics and apply security methods to biometric template data

Week 1


Lecture: Introduction
Lecture: Biometric Systems: Functionalities
Lecture: MATLAB Overview

Outcomes

  • Discuss the course outline and objectives
  • Explain what a biometric is
  • Define identity management
  • Compare methods of person recognition
  • Compare examples of biometrics and biometric systems
  • Evaluate the roles of components of a biometric system
  • Analyze the design of a generic biometric system
  • Compare biometric functionalities
  • Identify basic MATLAB program flow
  • Apply a variety of MATLAB functions
  • Create standard arrays
  • Apply array and matrix arithmetic
  • Apply image processing toolbox functions

Week 2


Lecture: Performance Evaluation: ROC Curves 
Lecture: Design Cycle
Lecture: Project Overview

Outcomes

  • Evaluate the significance of uniqueness and permanence in biometric traits
  • Identify the causes and implications of biometric system errors
  • Analyze the implications of a perfect match in biometric systems
  • Compare inter-user similarity and intra-user variation
  • Identify classification outcomes
  • Apply FNMR, FMR, FAR, FRR
  • Create ROC curves and apply them to biometric system evaluation
  • Examine the major steps of the biometric system design cycle
  • Discuss the scientific method
  • Compare attributes that depend on the nature of the application
  • Analyze the choice of a biometric trait in system design
  • Examine data collection
  • Identify the objectives and requirements of the course project

Week 3


Lecture: Fingerprint Recognition
Lecture: Fingerprint Feature Extraction: Poincaré
Lecture: Performance Evaluation: Fingerprint Matching

Outcomes

  • Explain why fingerprints are a viable biometric
  • Compare the different levels of fingerprint feature details
  • Evaluate the capabilities of each level of fingerprint features
  • Compare fingerprint acquisition approaches and technologies
  • Explain why certain features are practical for recognition
  • Apply the Poincaré method in order to classify singularities
  • Apply the method of minutiae detection
  • Explain the fingerprint template matching process
  • Apply ROC curves to set matching algorithm threshold
  • Apply ROC curves to evaluate matching performance

Week 4


Lecture: Face Acquisition: Detection
Lecture: Face Recognition

Outcomes

  • Compare the challenges and benefits of face recognition
  • Compare the different levels of facial features
  • Apply biometric system design to a face recognition system
  • Identify the modules of a face recognition system
  • Compare the different methods of face data acquisition
  • Explain the Viola-Jones face detection algorithm
  • Compare the main approaches to feature extraction in face recognition
  • Identify the main attributes of feature extraction techniques
  • Explore the challenges in comparing face recognition results

Week 5


Lecture: Iris Recognition

Outcomes

  • Explore the iris recognition system design
  • Compare iris image acquisition challenges and benefits
  • Explain the approaches to mitigating iris image acquisition challenges
  • Compare the integro-differential operation and GAC segmentation approaches
  • Examine the motivation for normalization
  • Examine the iris encoding and matching process
  • Identify approaches to assess the quality of iris images

Week 6


Lecture: Additional Biometrics

Outcomes

  • Analyze and create system designs for a variety of biometrics
  • Identify the general recognition approaches for the additional biometrics
  • Compare the utility of ear biometrics
  • Explore the challenges facing ear biometrics
  • Compare the benefits and challenges of gait recognition
  • Compare the benefits and challenges of hand geometry biometrics
  • Explain how soft biometrics can improve performance

Week 7


Lecture: Multibiometrics

Outcomes

  • Compare unibiometric and multibiometric systems
  • Identify the design challenges facing multibiometric systems
  • Classify multibiometric systems and understand each classification attribute
  • Compare serial and parallel acquisition and processing architectures
  • Compare biometric fusion methods
  • Apply score-level, rank-level, and decision-level fusion

Week 8


Lecture: Security

Outcomes

  • Compare unibiometric and multibiometric systems
  • Identify the design challenges facing multibiometric systems
  • Classify multibiometric systems and understand each classification attribute
  • Compare serial and parallel acquisition and processing architectures
  • Compare biometric fusion methods
  • Apply score-level, rank-level, and decision-level fusion

The course description, objectives and learning outcomes are subject to change without notice based on enhancements made to the course.