IDENTIFYING DATA 2020_21
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 francesc.serratosa@urv.cat
Lecturers
SERRATOSA CASANELLES, FRANCESC D'ASSÍS
Web
General description and relevant information <p> The information published in this guide corresponds to face-to-face classes and can serve as a guide. Due to the health emergency caused by COVID-19 there may be changes in teaching, assessment and calendars for the 2020-21 academic year. These changes will be reported in the Moodle space of each subject.</p><p>GENERAL DESCRIPTION OF THE SUBJECT:Study of methods for recognizing people through biometric techniques and the impact that these methods pose to our society . </p>

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 Forming opinions on the basis of the efficient management and use of information
 CT3 Solve complex problems critically, creatively and innovatively in multidisciplinary contexts.
 CT4 Work in multidisciplinary teams and in complex contexts.
 CT7 Apply ethical principles and social responsibility as a citizen and a professional.
Type C Code Competences Nuclear

Learning outcomes
Type A Code Learning outcomes
 A1 Analyse the problems and their causes from a global focus in the medium and long term.
 A4 Design real applications with databases and biometric access.
 G1 Integrate theoretical knowledge into the realities to which it may apply.
 G2 Apply the techniques learned in a specific context.
Type B Code Learning outcomes
 CT2 Master the tools for managing their own identity and activities in a digital environment.
Search for and find information autonomously using criteria of importance, reliability and relevance, which is useful for creating knowledge
Organise information with appropriate tools (online and face-to-face) so that it can be updated, retrieved and processed for re-use in future projects.
Produce information with tools and formats appropriate to the communicative situation and with complete honesty.
Use IT to share and exchange the results of academic and scientific projects in interdisciplinary contexts that seek knowledge transfer.
 CT3 Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution.
Follow a systematic method with an overall approach to divide a complex problem into parts and identify the causes by applying scientific and professional knowledge.
Design a new solution by using all the resources necessary and available to cope with the problem.
Draw up a realistic model that specifies all the aspects of the solution proposed.
Assess the model proposed by contrasting it with the real context of application, find shortcomings and suggest improvements.
 CT4 Understand the team’s objective and identify their role in complex contexts.
Communicate and work with other teams to achieve joint objectives.
Commit and encourage the necessary changes and improvements so that the team can achieve its objectives.
Trust in their own abilities, respect differences and use them to the team’s advantage.
 CT7 Be aware of gender and other inequalities in their activity as a URV student.
Analyse the major environmental problems from the perspective of their field of expertise in their student and/or professional activity.
Be able to give arguments based on social values and make proposals for the improvement of the community.
Be personally and professionally committed to applying the ethical and deontological concepts of their field of expertise.
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
Lecture
A1
A4
 17 25.5 42.5
IT-based practicals
A1
G1
G2
CT4
 10 15 25
Problem solving, exercises
G1
CT3
 4 6 10
Presentations / oral communications
A4
CT3
CT7
 1 1.5 2.5
Personal attention
6 0 6
 
Extended-answer tests
CT2
CT7
 6 18 24
 
(*) 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 Explicació del funcionament de l'assignatura. Relació de la Biometria en la societat actual.
Lecture Explicació de les tècniques bàsiques per al reconeixement de les persones a través de la Biometria (reconeixement de la cara, de l'iris, de la ditada i altres).
IT-based practicals Implementació d'un mètode senzill per al reconeixement de les persones a través d'un llenguatge de programació
Problem solving, exercises Problemes específics pel reconeixement de les persones per l'iris, la cara i la ditada.
Test de la qualitat dels sistemes biomètrics.
Presentations / oral communications Explicació d'un sistema concret o un algorisme dins d'un sistema.
Personal attention Consultes al despatx 243 i per correu electrònic

Personalized attention
Description
Consultes al despatx 243 i per correu electrònic

Assessment
Methodologies Competences Description Weight        
IT-based practicals
A1
G1
G2
CT4
 Lliurament d'una pràctica portada a terme en un grup de dos o tres persones. La nota serà individualitzada. 60%
Presentations / oral communications
A4
CT3
CT7
 Explicació d'un sistema concret o un algorisme dins d'un sistema. 20%
Extended-answer tests
CT2
CT7
 Tres proves teòriques individuals. 20%
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, , 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.