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FOUNDATIONS OF ARTIFICIAL VISION AND BIOMETRY

Michele NAPPI FOUNDATIONS OF ARTIFICIAL VISION AND BIOMETRY

0522500084
COMPUTER SCIENCE
EQF7
COMPUTER SCIENCE
2023/2024



YEAR OF COURSE 1
YEAR OF DIDACTIC SYSTEM 2016
SPRING SEMESTER
CFUHOURSACTIVITY
972LESSONS
MICHELE NAPPI T Curriculum
Objectives
KNOWLEDGE AND UNDERSTANDING

CRITICAL KNOWLEDGE OF THE FOUNDATIONS OF IMAGE BASED BIOMETRIC TECHNIQUES IN THE VARIOUS APPLICATION CONTEXTS.

APPLYING KNOWLEDGE AND UNDERSTANDING

THE STUDENT WHO WILL PROFITABLY FOLLOW THE COURSE:
•WILL KNOW THE MAIN ISSUES RELATED TO THE ANALYSIS AND SYNTHESIS OF BIOMETRIC SIGNALS;
• WILL BE ABLE TO UNDERSTAND THE LOGIC OF THE TRANSFORMS AND OPERATORS MOST WIDELY USED IN THE IMAGING CONTEXT;
• WILL BE ABLE TO CHOOSE THE TECHNIQUE BEST SUITED TO THE REFERENCE OPERATING ENVIRONMENT;
• WILL BE ABLE TO USE COMPARATIVE TOOLS TO MEASURE THE PERFORMANCE OF A GIVEN TECHNIQUE IN TERMS OF EFFICIENCY AND EFFECTIVENESS;
• WILL BE ABLE TO INDEPENDENTLY DESIGN AND IMPLEMENT IMPROVEMENT STRATEGIES BASED ON BASIC TECHNIQUES, IN PARTICULAR BY IDENTIFYING APPROPRIATE DESIGN SOLUTIONS FOR SPECIFIC MULTI-BIOMETRIC SYSTEMS;
• WILL BE ABLE TO SUPPORT CONVERSATIONS ON TOPICS RELATED TO THE CORE ASPECTS OF THE DISCIPLINE BY USING BOTH AN APPROPRIATE SCIENTIFIC TERMINOLOGY AND THE TOOLS OF MATHEMATICAL AND GRAPHIC REPRESENTATION OF THE MAIN DESCRIBED PHENOMENA;
• WILL PRODUCE MEDIUM-SIZED SOFTWARE PROJECTS IN PYTHON OR MATLAB
Prerequisites
STUDENT MUST HAVE BASIC KNOWLEDGE ABOUT BASIC ANALYSIS MATHS AND IMAGE PROCESSING.
Contents
1. IMAGE PROCESSING BASIC TECHNIQUES
2. BIOMETRICS (FACE, IRIS, FINGERPRINT, EAR)
3. ARTIFICIAL INTELLIGENCE AND COMPUTER IN COMPUTER VISION
4. MULTIBIOMETRIC SYSTEM: A CASE STUDY
Teaching Methods
CLASS LECTURES INCLUDES LECTURES (6 ECTS) AND LABORATORY PROGRAMMING PRACTICE (3 ECTS). IN THE LABORATORY PRACTICE HOURS THE TEACHER WILL PROVIDE EXAMPLES OF PROGRAM WRITING (PYTHON\MATLAB\C LANGUAGE). DURING THE EXERCISES THE STUDENT WILL ADDRESS THE PROBLEM SOLVING IN THE COMPUTER VISION CONTEXT USING MACHINE LEARNING AND DEEP LEARNING. THE RESOLUTION METHOD CONSISTS IN UNDERSTANDING THE PROBLEM, IN DESIGNING A SOLUTION, AND FINALLY IN ITS IMPLEMENTATION. THE LAST PHASE INVOLVES ASSESSING THE VALIDITY OF THE SOLUTION AND VERIFYING THE CONSISTENCY AS WELL AS EFFECTIVENESS AND EFFICIENCY IN A COMPARATIVE CONTEXT.
Verification of learning
THE EXAM IS AIMED AT EVALUATING THE STUDENT'S ABILITY TO LEARN THE BASIC PRINCIPLES OF COMPUTER VISION AND BIOMETRIC. THE STUDENT MUST DEMONSTRATE THAT HE HAS ACQUIRED THE ABILITY TO IDENTIFY THE MOST APPROPRIATE METHODOLOGICAL SOLUTION TO THE REFERENCE CONTEXT.
THE EXAM CONSISTS OF A LABORATORY TEST AND AN ORAL INTERVIEW. IN THE LABORATORY TEST INCLUDING ALSO A HOMEWORK ACTIVITY THE STUDENT HAS TO DESIGN, IMPLEMENT AND TEST IN COMPARATIVE MODE AN INNOVATIVE SOLUTION APPLIED TO THE BIOMETRIC CONTEXT. THE COMPARATIVE TEST INVOLVES SELECTING A PUBLIC DATABASE ON WHICH PERFORMANCE CAN BE MEASURED IN TERMS OF EFFICIENCY AND EFFECTIVENESS. THE SW USED FOR EXPERIMENTATION WILL BE MATLAB OR PHYTON. THE EXAM IS PASSED IF THE SCORE IS AT LEAST 18/30.
THE ORAL TEST WILL CONSIST OF AN INTERVIEW WHERE THE THEORETICAL AND FORMAL TOPICS DISCUSSED IN THE COURSE WILL BE DEALT WITH.
THE EVALUATION CRITERIA INCLUDE THE COMPLETENESS AND CORRECTNESS OF THE LEARNING AND THE CLARITY OF THE PRESENTATION.
Texts
LEARNING MATERIAL (HANDOUTS, SLIDES, EXERCISES) IS AVAILABLE ON-LINE TO THE STUDENTS (EL-PLATFORM).
THE FOLLOWING BOOK IS NECESSARY FOR THE INDIVIDUAL STUDY.

1.MODERNE TECNICHE DI ELABORAZIONE DI IMMAGINI E BIOMETRIA, M. NAPPI E D. RICCIO, ATHENA EDITORE, 2008
2.DIGITAL IMAGE PROCESSING (THIRD EDITION), RAFAEL C. GONZALEZ AND PAUL WINTZ, ADDISON WESLEY, 1994
More Information
•TEACHING E-MAIL: MNAPPI@UNISA.IT
•TEACHING WEB SITE:
WWW.UNISA.IT/DOCENTI/MICHELENAPPI/INDEX
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