01 - Introduction

About the project

  • choose a topic and make a software on it
    • you’re allowed to get any software as long as you modify enough
    • not forks, or forks + some other features
    • you must know all the topics in the syllabus


  • biometrics refers to the study and use of methods for detect and measure the characteristics of living organisms and draw comparatively classifications and laws
    • in CS: automatic identification or verification of the identity of a person based on physical or behavioral characteristics
      • identification: who are you
      • verification: what prove that you are who you declare
    • Biometric Consortium: automatic recognition of a person according to discriminative characteristics

What they does

  • biometric system are special systems that do pattern recognition
    • pattern recognition is at the basis on many topics, like computer vision
    • pattern recognition is a special model that reads information that is necessary to summarize the characteristics of some objects (features)
      • when we have features we can measure the distance between two objects (or class of objects) and see if they are enough similar
    • problems:
      1. what is a good distance measure
        • quick, easy to calculate
        • we compute the difference between the two similarities
      2. which are the best features
        • must be able to distinguish between two objects belonging to the same class
        • they’re good only if the can distinguish
      3. what is the difference margin to accept
        • it is a margin of uncertainty to agree that two objects are similar

Classies and patterns

We distinguish between classies and pattern in several ways:

  • content based image retrieval

    • classies: type of objects
    • pattern: any kind of features that allow to distinguish between a face and a flower
  • Recognition

    • classies: subclasses of same type
    • pattern: allows to distinguis a subclass
  • Biometrics

    • classies: individuals
    • pattern: allows to distinguish among individuals

Uniqueness of a person

  • basic assumption: each person is unique

    • it’s true, but we all share a lot of similiarities (features)
  • main issues

    • determine the unique features able to identify a person
    • find reliable techniques to measure such features
    • devise reliable algorithms to recognize/classify a person according to the measured features