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Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title Biometrics
Module Code CA641
School School of Computing
Online Module Resources

Module Co-ordinatorSemester 1: John McKenna
Semester 2: Alistair Sutherland
NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Description
This module aims to examine modern approaches to automatically identifying or verifying an individual based on one or more biometric traits, e.g. speech, DNA, fingerprints. It will cover all or most aspects of a biometric recognition from data acquisition through signal processing to pattern recognition. Students taking this model will already be able to program, have solid mathematical competence, and will have an interest in any or all of: biometrics, pattern recognition, signal processing.

Learning Outcomes
1. Explain the operations and distinguishing features of elements of a biometric recognition system
2. Describe the primary techniques and technologies used in the common biometrics, e.g. speech, DNA, fingerprints
3. Use mathematics appropriate to biometric processing
4. Process novel data in algorithms used in biometric recognition systems
5. Evaluate the appropriateness of different biometric recognition technologies
6. Design and implement a biometric recognition system
7. Communicate experimental and design work in formal writing while citing the work of others which has contributed to the student's work



Workload Full-time hours per semester
Type Hours Description
Lecture36Face-to-face and online screencasts
Lecture36Face-to-face and online screencasts
Laboratory24Assignment or set exercises
Laboratory24Assignment or set exercises
Moodle discussion6Queries, Replies, Monitoring
Assignment40Individual Projects
Moodle discussion6Queries, Replies, Monitoring
Independent learning81At individual's discretion
Assignment40Individual Projects
Independent learning81At individual's discretion
Total Workload: 374

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities
Generic mathematical techniques:.
Review of probability, statistics, linear algebra..

Usage issues of biometric systems:.
Benefits and risks; Costs; Privacy..

System architecture:.
Data acquisition; Signal processing; Feature extraction; Enrollment - model/template creation; Recognition/Acceptance decisions..

Digital signal processing:.
Time and space frequency-domain analysis; Feature extraction and preprocessing; Principal Component Analysis..

Pattern Recognition:.
Probabilistic models and distance-based templates and metrics; Accuracy, false acceptance rates, false rejection rates; Various algorithms relating to - Dynamic Programming, Gaussian classifiers, Hidden Markov Models, Support Vector Machines, Sequence matching..

Overview of the primary biometrics:.
Speech; Fingerprint; Face; Iris, DNA..

Other:.
Signatures and key-strokes; Multifactor identification.

Assessment Breakdown
Continuous Assessment30% Examination Weight70%
Course Work Breakdown
TypeDescription% of totalAssessment Date
In Class TestAssessment of Infrastructural Mathematics10%Week 4
Practical/skills evaluationPlanning, design, implemntation and evaluation of a basic biometric recognition system; Formal write-up; Review10%Week 8
OtherMATLAB-based exercises5%Week 10
OtherDNA-related assessed exercises5%Week 12
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 3
Indicative Reading List
    Other Resources
    None
    Array
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    Timetable this semester: Timetable for CA641
    Date of Last Revision25-AUG-11
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