Biometry

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