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Chapter 11 Integration Information

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

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Chapter 11 Integration Information

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  1. Chapter 11Integration Information Instructor: Prof. G. Bebis Represented by Reza Fall 2005

  2. 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

  3. 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?

  4. 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:

  5. 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

  6. 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

  7. 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”.

  8. 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.

  9. Boolean combination • OR • Improve convenience (lower the FRR) • AND • Improve security (lower FAR)

  10. 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 ( )

  11. 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

  12. 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: • 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

  13. Binning and Filtering • Penetration rate or filter rate: • Binning error rate 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

  14. 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 and a knowledge match between the collected responses through speech recognition and the knowledge

  15. Score level integration • Loosely coupled integration at score level integration • Assumption: • Scores are normalized • B is the measurements of biometric a and b

  16. Score level integration • Principle cases: • The scores are monotonically related to the likelihood • The scores are related to the likelihood in more complex fashion • Given ground truth marked data, it is possible to determine a function which relates to • Using ground truth data to estimate joint probability density function • Example: Prabhakar and Jain estimate the conditional densities using non-parametric estimation method

  17. Normal distribution • Approximation of the curve G with a linear function • This approximation is correct only when both and are normally distributed. • If we assume that and are independent, the above equation becomes:

  18. 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:

  19. Degenerate cases • If then • If then • It means biometric a does not contribute much to class separation.

  20. 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 and the cumulative mismatch score distribution

  21. From thresholds to boundaries • FR estimates are now the value of along the curve • The FRR are given by • A multi biometric system should not just be associated with one FAR and one FRR but with one FAR and a sequence of

  22. 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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