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

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

Recognition with geometrical features

• geometrical features are extracted from face
  • simple extraction, difficult classification
• possible features
  • horizontal and vertical projection of parts (eyes,nose, . . .)
  • position, central point, width, thickness, radius of parts of the face

"Eigenfaces"

• beginning: amount of faces:
  • 1. mean value calculation of the sample faces
  • 2. mean value from step 1 subtracted from every sample face
  • 3. “Kovarianzmatrix” calculation of normalized images from step 2
  • 4. mathematical procedure gives optimal base of Eigenfaces
• base for effizient facial recognition / data compression
• condition: all faces to be recognized were trained before and represented: (calculation of “Eigenfaces-Base”)
• recognition of an image
  • projection into “Eigenfaces-Base”
  • Comparison of resulting values
  • assign to registered face (person) thats value is next to
• problem: classify unknown faces

3D Recognition

• Features:
  • developed in israel
  • further development at Siemens/Munich
  • “contoured face mask” is built up
  • superior quality compared with 2D images
  • very fast and reliable
  • does not care about the position of the face
• How it works:
  • project coloured lines
  • at varying heights no straight lines
  • position of each pixel is calculated to geometric data
  • (3D Image at precision of 0.2 x 0.2 x 0.2mm within 40 ms)
  • image analysed by using classical recognition methods
  • ! more secure
  • ! impossible to cheat the system by placing a photo in front of the camera

Summary

• Evaluation and Comparison
  • feature based approaches
    • can handle different orientations of faces
    • manual definition of features
    • automatical extraction of features / classification not very reliable
  • appearance based approaches
    • for every view separate image of the person is necessary
    • simple to realize, automatical feature extraction

Insights on Fingerprints

• Features found on fingers
• Acquisition of fingerprints
• Recognition of fingerprints
• Some demonstration
• Problems of fingerprints

Classification of fingerprints

In the Henry system of fingerprint classification there are five different classes of fingerprints:

• Right Loop
• Left Loop
• Whorl
• Arch
• Tented Arch

The frequency of each class seems to vary with ethnic origin: Europeans have mostly loops, Pygmies and African bush men mostly have archs and Orientals have plenty of whorls.

Minutiae

Different types of minutiae:

• endings
• bifurcations

Devices

There’s a plethora of different sensor types:

• capacitive
• optical (several principles)
• thermic
• ultrasonic
• pressure

Acquisition methodology: touching vs. sweeping

Typical extraction flow

• fingerprint image
• binarization
• thinning
• minutiae extraction
• post processing
• minutiae

Problems

Encryption . . . Fingerprints

• are easy to steal
• can’t be changed once they are stolen
• are easy to fake on the most common sensor devices
• are usually not universally applicable (fer 5%)