Biometry

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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%)