IDENTIFYING DATA 2016_17
Subject (*) BIOMETRIC IDENTIFICATION Code 17685103
Study programme
Computer Security Engineering and Artificial Intelligence (2016)
 Cycle 2nd
Descriptors Credits Type Year Period
4.5 Compulsory First 1Q
Language
Anglès
Department Computer Engineering and Mathematics
Coordinator
SERRATOSA CASANELLES, FRANCESC D'ASSÍS
 E-mail
Lecturers
Web
General description and relevant information Study of methods for recognizing people through biometric techniques and the impact that these methods pose to our society

Competences
Type A Code Competences Specific
 A1 Integrate the fundamental technology, applications, services and systems of Computer Security and Artificial Intelligence,in a broader, multidisciplinary context.
 A4 Design, develop, manage and evaluate mechanisms to certify and guarantee security in handling information and access to it in a local or distributed processing system.
 G1 Project, calculate and design products, processes and installations in the areas of Computer Security and Artificial Intelligence
 G2 Perform mathematical modelling, calculation and simulation in company technology and engineering centres, particularly in tasks of research, development and innovation in the areas of Computer Security and Artificial Intelligence
Type B Code Competences Transversal
 CT2 Formular valoracions a partir de la gestió i ús eficient de la informació.
 CT3 Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars.
 CT4 Treballar en equips multidisciplinars i en contextos complexes.
 CT7 Aplicar els principis ètics i de responsabilitat social com a ciutadà i com a professional.
Type C Code Competences Nuclear

Learning outcomes
Type A Code Learning outcomes
 A1 Analitza els problemes i les seves causes des d'un enfocament global i de mitjà i llarg termini.
 A4 Dissenya aplicacions reals amb bases de dades i accés biomètric.
 G1 Integra els coneixements teòrics amb les realitats a les quals es poden aplicar.
 G2 Aplica les tècniques apreses en contextos concrets.
Type B Code Learning outcomes
 CT2 Formular valoracions a partir de la gestió i ús eficient de la informació.
 CT3 Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars.
 CT4 Treballar en equips multidisciplinars i en contextos complexes.
 CT7 Aplicar els principis ètics i de responsabilitat social com a ciutadà i com a professional.
Type C Code Learning outcomes

Contents
Topic Sub-topic
1: Biometrics for recognition of persons
2: Evaluation of Biometric Systems in Real Applications
3: Recognition of Persons by Fingerprint
4: Recognition of People by Face
5: Identification of Persons by Iris
6: Security in biometric systems

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
 Hours outside the classroom
 (**) Total hours
Introductory activities
1 1.5 2.5
Presentations / expositions
CT2
CT7
 1 1.5 2.5
Assignments
A1
G1
G2
CT4
 13 19.5 32.5
Forums of discussion
CT2
CT7
 1 1.5 2.5
Material reading and studying
A1
A4
 25 37.5 62.5
Personal tuition
1 0 1
 
Objective multiple-choice tests
A4
CT3
CT7
 3 6 9
 
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies
  Description
Introductory activities
Presentations / expositions
Assignments
Forums of discussion
Material reading and studying
Personal tuition

Personalized attention
Description
Consultes per correu electrònic

Assessment
Methodologies Competences Description Weight        
Presentations / expositions
CT2
CT7
 20%
Assignments
A1
G1
G2
CT4
 70%
Objective multiple-choice tests
A4
CT3
CT7
 10%
Others  
 
Other comments and second exam session

Sources of information

Basic Maltoni, Maio, Jain, Prabhakar, Handbook of fingerprint recognition, Springer, Any 2009, ISBN 978-1-84882-253-5
Li et al, Handbook of Face Recognition, Springer, , Springer, Any 2005, ISBN 0-387-40595-X
Kaushik Roy & Prabir Bhattacharya,, "Iris Recognition. A Machine Learning Approach, Editorial Verlag Dr. Muller, , Any 2008, ISBN 978-3-639-08259-3

Complementary

Recommendations
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.