Biometry: Difference between revisions

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*Facial Recognition: find face, find certain parts, calculate BG and compare graphs
*Facial Recognition: find face, find certain parts, calculate BG and compare graphs
*save useful variation
*save useful variation

=Deformable Templates=
*analyze face with geometrical figures
*alignment, shape, size, but also especially the position of the elements to each other
*samples (templates) are adapted to concrete face
*notes
**Templates comparable with LG; has to be adapted to concrete face
**Extraction of edges necessary; not very reliable

Revision as of 13:21, 15 February 2005


Overview

  • Broad Overview on Biometric Criteria
  • Facial recognition
    • Use, Scenario, Features
    • General course
    • Approaches
    • Summary
  • Insights on Fingerprints
    • Features
    • Acquisition
    • Recognition
    • Demonstration
    • Problems

Short Introduction

  • I biometry: the statistical analysis of biological

observations and phenomena (Merriam-Webster Online Dictionary)

  • I biometric: a measurable, physical characteristic or

personal behavioral trait used to recognize the identity, or verify the claimed identity of a person

  • I Three levels:
    • 1. I know something (passwords)
    • 2. I have something (tokens)
    • 3. I am something (biometry)

Classification

Two diffent modes must be distinguished:

  • Identification “Who is it?”
  • Authentication “Is it her?”

Quality indices

False Rejection Rate (frr)

  • Rate of authentication attempts from legitimate users that are rejected.

False Acceptance Rate (far)

  • Rate of authentication attemps from illegitimate users (e.g. attackers) that are accepted.

Equal Error Rate (eer)

  • The eer equals the far and frr when the system is configured so that far = frr.

Failure to Enroll Rate (fer)

  • Rate of users that can’t enroll into the system at all.

Hand geometry

  • Hand is placed on a reflective surface
  • Hand is lit by some light source (e.g. bright LED)
  • a camera and some mirrors are used to gather images of several parts of the hand from several angles
  • the template consists of measurements extracted from the image

Retina scanning

  • was available before iris scanning
  • good (e.g. low) far
  • a low intensity light source is used to scan the vascular pattern

Other techniques

  • voice verification
  • signature verification
  • vein pattern scanning
  • scent
  • keystroke patterns

Use

  • artifical intelligence
    • autonomic systems should interact with human, recognize and interpret
    • facial expressions as essential component
  • civil authentification
    • drivers licence, credit cards, access
    • inconspicuous, good acceptance by users expected
    • question: reliability, frontiers (twins)
  • identification in delinquency (crime)
    • cameras, automatical identification of suspects
    • support search for missing persons

Scenario

  • controlled environment
    • uniform head position, cooperative users, good

image quality

  • uncontrolled environment
    • face has to be found, position, orientation, size, illumination (shadows), bakground, are variable and have to be compensated
    • facial expression (laughing) has big influence
    • face partially hidden, beard, hairstyle, glasses, hat can hide parts of the facial information
    • injuries, holiday, age change the face


Definition of Features

  • before identification
    • face has to be found and separated from the background
    • separating background and illumination are big problems
  • manual definition of features
  • calculation of features often not reliable
  • amount of manually defined features too small to separate different people
  • immense variety of features has to be expected


Generating Templates

comparison

  • different procedures are used
  • general:
    • byte-comparison of two templates
    • use of algorithms (vector operations)
    • grade of similarity
    • if within tolerance then identical
  • algorithms:
    • template matching
    • elastical bunch graph matching
    • geometrical features
    • “eigenfaces”


Template Matching

  • similarity between image and template is calculated
  • template: for whole face or parts (eye, mouth, nose)
  • unknown face is compared with all that were saved
  • problems
    • mask has to match for a lot of faces
    • a lot of time necessary, quality depends from mask

Elastic Bunch Graph Matching

  • Labeled Graph (LG): nodes at certain parts of face (nose, mouth,..); universal face graph
  • Bunch Graph (BG): every node gets different features as a subgraph (specializing the LG)
  • Facial Recognition: find face, find certain parts, calculate BG and compare graphs
  • save useful variation

Deformable Templates

  • analyze face with geometrical figures
  • alignment, shape, size, but also especially the position of the elements to each other
  • samples (templates) are adapted to concrete face
  • notes
    • Templates comparable with LG; has to be adapted to concrete face
    • Extraction of edges necessary; not very reliable