View source for 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