Integration Information by sofiaie

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									         Chapter 11

Integration Information
   Instructor: Prof. G. Bebis
     Represented by Reza

            Fall 2005
                    Outlines
• Introduction
• Integration methods
• Decision level integration
  – Boolean combination
  – Binning and filtering
  – Dynamic authentication protocols
• Score level integration
  – Normal distributions
  – Degenerate cases
  – From threshold to boundaries
• Alternative
                   Introduction
• For many application there are non-biometric sources of
  information that can be used in person authentication.

• Using a single biometric is not sufficiently secure or does
  not provide sufficient coverage of the user population.

• Question: How do we integrate multiple biometric
  sources of information to make the application more
  accurate and more secure?
               Integration methods
• There are many different methods that can be used to expand a
  biometric system:
   –   Multiple biometrics: Face image and voiceprint
   –   Multiple location: Left and right iris
   –   Multiple sensing: Three tries of index finger
   –   Multiple sensors: Ultrasonic and optical sensing
   –   Multiple matchers: Minutia and correlation fingerprint matcher
   –   Multiple token: Adding possession and/or knowledge

• An example using multiple biometrics:
                 Integration methods
•   Regardless of the methods, there are tow basic approaches to combination
    information from different sources:
      – Tightly coupled integration:
         • A strong interaction among the input measurements and integration schemes.
     – Loosely coupled integration:
         • There is no interaction among inputs and integration occurs at the output.




                    A tightly coupled system       A loosely coupled system
          Integration methods
• Loosely coupled integration systems
  advantages:
  – Simpler to implement
  – More feasible in commonly confronted integration
    scenario
• One could try to integrate the biometrics at tow
  levels:
  – Decision level
  – Score level
       Decision level integration
• Decision level integration (fusion) is typically concerned
  with multiple matchers method.
• Methods:
   – Boolean combination
   – Binning and filtering
   – Dynamic authentication protocols
• Note that in this context, the matchers are considered as
  block box and each output is simply a “Yes/No”.
           Boolean combination
• The AND rule and the OR rule:
   – the most prevalent rules for multiple biometrics combination in
     practical systems.




   – Used in both Identification and Verification protocol.
        Boolean combination
• OR
  – Improve convenience (lower the FRR)
• AND
  – Improve security (lower FAR)
           Binning and Filtering
• The capability of a biometric 1:many search of a
  database is a prerequisite.
• Performing N biometric 1:1 matches :
   – Advantage:
      • Simple way.
   – Disadvantage: (When N, the size of the database, becomes
     large)
      • High computational cost
      • High False Positive Rate and large candidate list (FPR N)
                      Filtering
• Constraining the search with parametric (non-
  biometric) data is called “filtering”.
  – Filtering down the search of N by, say, a subject’s
    surname
  – Filtering is an authentication protocol
              (Possession, Biometric)=(P, B)=(name, B)
     • Search the database N of enrolled subjects by surname
     • Search the subjects with matching surname P by matching
       input sample biometric B
                        Binning
• Constraining the search with additional biometric
  data is referred to as “binning”.
  – The best known instance is first classify the type of
    fingerprint and then match the minutia of fingerprint
  – Authentication protocol:
                                     B, B
     • Select those subjects in database N whose biometric
       template matches biometric B’
     • Match the input biometric template B with the templates of
       those remaining subjects to find those subjects in N with both
       matching B’ and B
            Binning and Filtering
• Penetration rate or filter rate:
                        E (# of times B is matched)
                  Ppr 
                                     N
• Binning error rate Pbe is the percentage of subjects in the
  data base that are missed classify
• Possession token can be added to an existing
  authentication protocol:
   – Negative identification
       • Decrease the chance of False Positive
       • Increase the probability of False Negative dramatically
   – Positive identification
       • Does not decrease the biometric verification error rates
  Dynamic authentication protocols
• One dynamic protocol for speaker verification is the idea
  of conversational biometrics
• Conversational biometrics does a biometric match
  between the speech sample B and the voiceprint Bm and
  a knowledge match between the collected responses
  through speech recognition and the knowledge K m
         Score level integration
• Loosely coupled integration at score level integration
• Assumption:
   – Scores are normalized sa , sb  0,1
   – B is the measurements of biometric a and b
          Score level integration
• Principle cases:
   – The scores ( sa , sb )are monotonically related to the likelihood P(dm B)
   – The scores ( sa , sb )are related to the likelihood P(dm B) in more
     complex fashion
• Given ground truth marked data, it is possible to
  determine a function which relates ( sa , sb ) to P(dm B)
   – Using ground truth data to estimate joint probability density
     function P(sa , sb Ho ) and P(sa , sb H a )
   – Example: Prabhakar and Jain estimate the conditional densities
     using non-parametric estimation method

                                        P( sa , sb H o )
                           P(d m B) 
                                        P( sa , sb H a )
                    Normal distribution
• Approximation of the curve G
  with a linear function
      G ( sa , sb )  sa  (1   ) sb  T
• This approximation is correct
  only when both and s a are s b
  normally distributed.
     – If we assume that s aand               s b are
       independent, the above
       equation becomes:
                                sa          sb
             G ( s a , sb )            
                                   2
                                    a        b2
           Normal distribution
• Problems:
  – The covariance matrix is assumed to be diagonal.
    This is good if disparate biometrics are used.
  – Modeling match scores with Gaussian is not realistic
     • Solution: Use of a Gaussian model for the probability
       distribution of the distance between two biometric templates
         – Simple example:     Dist ( B, Bm )  1  s( B, Bm )
                                                                      2
                                                     1  d  Em   
                                                                
                                            1        2  m       
                                P(d )          e                

                                           2 

                Em  E ( Dist ( B, Bm ))          m   ( Dist ( B, Bm ))
                Degenerate cases
• If  a   b then
                         sa
         G ( sa ,0)             T
                            2
                             a

• If  b   a then
                         sb
          G (0, sb )            T
                            2
                             b


• It means biometric a does
  not contribute much to
  class separation.
  From thresholds to boundaries
• Question: Is there any way to
  estimate some decision
  boundary?
• Answer: Drive estimates of the
  match and mismatch score
  cumulative distributions from
  training data and determine
  operating point T that satisfy
  the design criteria.
• Assume that we have the
  cumulative match score
  distribution F ( sa , sb )                             FAR( sa , sb )  
                                     G( sa , sb )  
                                                    1  FAR( sa , sb )  1    T
  and the cumulative mismatch
  score distribution G ( sa , sb )
  From thresholds to boundaries
• FR estimates are now the
           
  value of
         
           F ( sa , sb ) along the
  curve G(sa , sb )  T
• The FRR are given by
        k k
       F ( sa , sb ) , k  1,..., K
• A multi biometric system
  should not just be
  associated with one FAR
  and one FRR but with one
  FAR and a sequence of FRR k
                   Alternative
• The OR and AND rules for combining two decisions are
  the only way
• When more than two decision are need to be combined
  the choice of fusion is not merely limited to applying an
  overall OR and AND rules to all decision
• Another method is voting
• Kittler and Alkoot have shown that score combination
  strategy are superior when the individual matcher scores
  follow Gaussian distribution and voting is superior when
  scores distribution are heavy tailed

								
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