UNIVERSITY OF KENT – CODE OF PRACTICE FOR QUALITY ASSURANCE
MODULE SPECIFICATION TEMPLATE
1 The title of the module
Advanced Biometric Systems
2 The Department which will be responsible for management of the module
Electronics
3 The Start Date of the Module
September 2006
4 The number of students expected to take the module
30
5 Modules to be withdrawn on the introduction of this proposed module and consultation with other relevant Departments and Faculties regarding the withdrawal
EL845 Advanced Biometrics: Original 30 credit module is being divided into two, 15 credit modules
6 The level of the module (eg Certificate [C], Intermediate [I], Honours [H] or Postgraduate [M])
M
7 The number of credits which the module represents
15
8 Which term(s) the module is to be taught in (or other teaching pattern)
2
9 Prerequisite and co-requisite modules
EL843 and EL845A
10 The programmes of study to which the module contributes
MSc Information Security and Biometrics
11 The intended subject specific learning outcomes and, as appropriate, their relationship to programme learning outcomes
On successful completion of the module, students will:
1. Have acquired the ability to design and implement biometric systems using a number of SDKs.
2. Have exposure to alternative applications of biometrics
3. Have gained an understanding of the operation of advanced pattern classification techniques involving multi-modal systems.
These outcomes are related to the programme learning outcomes in the Information Security and Biometrics MSc curriculum map as follows: A1, A2, A3, B1, B2, B3, C1, C2, C3, C6, C7 and C8.
12 The intended generic learning outcomes and, as appropriate, their relationship to programme learning outcomes
Students will demonstrate ability in generating, analysing, presenting and interpreting data, will learn to use ICT, and will develop core key skills, such as learning effectively, critical thinking and time management, contributing to the Transferable/Key Skills in the generic learning outcomes for the MSc programme D1, D2, D3, D4, D5 and D6.
13 A synopsis of the curriculum
Advanced techniques for feature classification and multi-modal systems: Analysis of Bayesian Classifier, Further feature selection strategies using genetic algorithms and Principal Component Analysis, Multiple classifier combination strategies. Intelligent and dynamically adaptable classification techniques, Multi-modal biometric systems and score normalisation techniques.
14 Indicative Reading List
1. A. Jain, R. Bolle, S. Pankanti (eds.). Biometrics – Personal Identification in a Networked Society. Kluwer Academic Publishers. 1999.
2. John D. Woodward Jr., Nicholas M. Orlans, Peter T. Higgins. Biometrics. Osborne. McGraw-Hill. 2003.
3. Bir Bhanu, Xuejun Tan. Computational Algorithms for Fingerprint Recognition. Kluwer Academic Publishers. 2003.
4. Arun A. Ross, Anil K. Jain, David Zhang. Multimodal Biometrics: Human Recognition Systems. Springer-Verlag. 2005.
5. James Wayman, Anil Jain, Davide Maltoni, Dario Maio (eds). Biometric Systems : Technology, Design and Performance Evaluation. Springer-Verlag UK. 2002.
6. Anil Jain, S.Z. Li (Eds). Handbook of Face Recognition. Springer-Verlag. 2005.
7. L. I. Kuncheva. Combining Pattern Classifiers – Methods and Algorithms. John Wiley and Sons. 2004.
8. S. Theodoridis, K. Koutroumbas. Pattern Recognition. Elsevier. 2003.
Background Reading:
1. S. M. Ross. Introduction to Probability and Statistics for Engineers and Scientists. (2nd edn). Harcourt Academic Press. 2000.
+ selected articles from the published technical literature
15 Learning and Teaching Methods, including the nature and number of contact hours and the total study hours which will be expected of students, and how these relate to achievement of the intended learning outcomes
Students will be presented with 15 lectures covering advanced pattern classification techniques which underlie the operation of modern biometric systems. These will be associated with four, six hour workshops. These will address learning outcomes 1, 2 and 3.
Teaching Summary:
Student Contact Hr / Student Workload HrLecture: Advanced techniques for feature classification and multi-modal systems / 15 / 60
Laboratory Experiments (Pattern classification) / 24 / 90
39 / 150
16 Assessment methods and how these relate to testing achievement of the intended learning outcomes
80% of marks will be awarded for an end of module examination which will test learning outcomes 1, and 3.
20% of marks will be awarded for the 8 practical workshops which will test learning outcome 2 and 3.
17 Implications for learning resources, including staff, library, IT and space
The business plan for the MSc includes a component to establish a new dedicated Biometrics Laboratory in the Department of Electronics in which all the teaching will take place. This laboratory will be fully equipped with PCs and relevant software.
18 A statement confirming that, as far as can be reasonably anticipated, the curriculum, learning and teaching methods and forms of assessment do not present any non-justifiable disadvantage to students with disabilities
No disadvantage to students with disabilities is anticipated.
Statement by the Director of Learning and Teaching: "I confirm I have been consulted on the above module proposal and have given advice on the correct procedures and required content of module proposals"
......Director of Learning and Teaching / ......
Date
Statement by the Head of Department: "I confirm that the Department has approved the introduction of the module and will be responsible for its resourcing"
......Head of Department / ......
Date
Revised August 2002; Revision 2 in 2003.