1. Biometrics

2. Topics Biometric identifier classification
Biometric identifier characteristics comparison
Multimodal Biometrics
Biometric Standards
Challenges in Biometrics

3. Identifiable biometric characteristics
Biological traces
DNA, blood, saliva, etc.
Biological (physiological) characteristics
fingerprints, eye irises and retinas, hand palms and geometry, and facial geometry
Behavioral characteristics
signature, gait, keystroke dynamics, lip motion, voice

4. Classification of identifiers
Physiological biometric identifiers: fingerprints,
hand geometry,
eye patterns (iris and retina),
facial features
and other physical characteristics.
Behavioral identifiers:
voice,
signature
typing patterns
other.
Analyzers based on behavioral identifiers are often less conclusive due to limitations/complex patterns.

5. Example of banking application

6. Biometric identifiers
Courtesy of G. Bromba

7. Biometric Market Share

8. Comparison of biometric techniques

9. Palm

10. Hand vein

11. Facial Thermogram

12. Ear print

13. Retina Human eye has its own totally unique pattern of blood vessels.
Because of its internal location, the retina is protected from variations caused by exposure to the external environment (unlike fingerprints).

14. Which Biometric is the Best?
Universality (everyone should have this trait)
Uniqueness (everyone has a different value)
Permanence (should be invariant with time)
Collectability (can be measured quantitatively)
Performance (achievable recognition accuracy, resources required, operating environment)
Acceptability (are people willing to accept it?)
Circumvention (how easily can it be spoofed?)

15. Selecting a Biometric
Selecting the ‘right’ biometric is a complicated problem that involves more factors than just accuracy. It depends on cost, error rates, computational speed, acquitability, privacy and easy of use.

16. Ideal Biometric Characteristics
The ideal biometric characteristics have five qualities:
Robust: Unchanging on an individual over time.
Distinctive: Showing great variation over the population.
Available: The entire population should ideally have this measure in multiples.
Accessible: Easy to image using electronic sensors.
Acceptable: People do not object to having this measurement taken on them.

17. Quantitative measures
Quantitative measures of these five qualities have been developed.
"Robustness" is measured by the "false non-match rate" (Type I error), the probability that a submitted sample will not match the enrollment image.
"Distinctiveness" is measured by the "false match rate" (Type II error), the probability that a submitted sample will match the enrollment image of another user.
"Availability" is measured by the "failure to enroll" rate, the probability that a user will not be able to supply a readable measure to the system upon enrollment.
"Accessibility" can be quantified by the "throughput rate" of the system, the number of individuals that can be processed in a unit time, such as a minute or an hour.
"Acceptability" is measured by polling the device users.

18. Biometric System Goals
A biometric system can be designed to test one of only two possible
hypotheses:
The submitted samples are from an individual known to the system
The submitted samples are from an individual not known to the system
Applications to test the first hypothesis are called "positive
identification" systems while applications testing the latter are
called "negative identification" systems.

19. Types of Biometrics
Overt Versus Covert: The first partition is "overt/covert". If the user is aware that a biometric identifier is being measured, the user is overt. If unaware, the use is covert. Almost all conceivable access control and non-forensic applications are overt. Forensic applications can be covert.
Habituated Versus Non-Habituated: This applies to the intended users of the application. Users presenting a biometric trait on a daily basis can be considered habituated after a short period of time. Users who have not presented the trait recently can be considered "non-habituated".
Attended Versus Non-Attended: This partition refers to whether the use of the biometric device during operation will be observed and guided by system management.
Open Versus Closed: If a system is to be open, data collection, compression and format standards are required. A closed system can operate perfectly well on completely proprietary formats.

20. Generic Biometric System
A generic biometric system.

21. Multimodal Biometrics
Multimodal Biometric system is a system that uses more than one independent or weakly correlated biometric identifier taken from an individual (e.g., fingerprint and face of the same person, or fingerprints from two different fingers of a person)

22. Multi-modal Systems: Fusion
Early integration or sensor fusion
Integration is performed on the feature level
Classification is done on the combined feature vector

23. Multi-modal Systems: Fusion
Late integration or decision fusion
Each modality is first pre-classified independently
The final classification is based on the fusion of the outputs of the different modalities

24. Multimodal biometrics systems
Multimodal biometrics systems improve performance
A combination in a verification system improves system accuracy
A combination in an identification system improves system speed as well as accuracy
A combination of uncorrelated modalities (e.g. fingerprint and face, two fingers of a person, etc.) is expected to result in a better improvement in performance than a combination of correlated modalities (e.g. different fingerprint matchers)

25. Other work: classification
FBI Fingerprint card (includes information on gender, ethnicity, height, weight, eye color and hair color)
Wayman (1997) proposed filtering large biometric databases based on gender and age
Givens et al. (2003) and Newham (1995) showed that age, gender and ethnicity can affect the performance of a biometric system

26. International Standards Bodies

27. Application Programming Interface (API)
Biometrics is the automated use of physiological or behavioral characteristics to determine or verify an identity
Standards for interfaces and methods for performance evaluation are needed

28. Biometric Authentication Systems
Layers of interaction with biometric authentication systems
Scope
Standardization of generic biometric technologies to support interoperability and data interchange between applications and systems
Included: common file formats, application programming interfaces (APIs), biometric templates, template protection techniques, related application/implementation profiles, methodologies for conformity

29. Basic Standards BioAPI – The most popular API in the biometrics area
CBEFF – Common Biometric Exchange File Format
ANSI X – Biometric Information Management and Security for the Financial Services Industry
ISO/IEC – Biometric Data Interchange Formats

30. Challenges in Biometrics
Large number of classes (~ 6 billion faces)
Large intra-class variability
Small inter-class variability
Segmentation
Noisy and distorted images
Population coverage & scalability
System performance (error rate, speed, cost)
Attacks on the biometric system
Every biometric characteristic has some limitations

31. Threats to Biometrics
The Modern Burglar

32. Matsumoto’s Technique
Only a few dollars’ worth of materials

33. Making the Actual Clone
You can place the “gummy finger” over your real finger. Observers aren’t likely to detect it when you use it on a fingerprint reader.
Don’t try this at home! (Matsumoto)

34. Summary
There is wide variety of biometric identifiers that posses different characteristics
Each biometric system should take into account the end goal of application
Multi-biometrics improve performance of individual matchers and is active topic of current biometric research
Biometric standards are being developed, while biometric reliability is still a concern
Signal Processing Institute, Swiss Federal Institute of Technology

35. Reference and Links www.sciencedierect.com
Signal Processing Institute, Swiss Federal Institute of Technology
Biometric Systems Lab, University of Bologna
Textbooks 1 and 2 CPSC
Signal Processing Institute, Swiss Federal Institute of Technology