1. Introduction to Biometric Systems
ECE 1518-JIE1001
Lecture 4
Introduction to Biometric Systems

2. Biometric systems (Objective)
What is a Human Biometric System?
A pattern recognition system that relies on physiological or behavioral
human characteristics to provide recognition of a person’s identity.
Verification mode (one-to one): An individual’s (claimant) identity is
verified by comparing “on –spot” acquired biometric data to stored
biometric data of the claimant from a database.
Positive recognition: Prevent multiple claims of an identity
False positive: Ease to impersonate
Identification mode (one-to many): An individual is recognized by
by comparing his/her biometric data to those of many individuals
stored in a database ( system fails if corresponding data are not
available in database).
Negative recognition: Prevent use of multiple identities
False negative: Avoid identification
A more general objective: to provide a user-centric authentication system
for identity/data verification or encryption

3. Applications Forensic applications (low false negative performance)
law enforcement, criminal records
Civilian applications (security and privacy)
Business, financial transactions, Government
Medical applications (privacy and confidentiality)
medical records, prescriptions, diagnostics
High Security applications (low false positive performance)
Immigration, Access control, military,surveilance

4. Historical Perspective (1)
From the Greek words bio (life) and metrics (measure)
29,000 BC Cave paintings “handprints” used as signatures.
500 BC Babylonian Business transactions in clay tablets include fingerprints.
Chinese used fingerprints and footprints to differentiate children.
mid 1800 AC “Bertillon”system of body dimensions is introduced in France to identify criminals “anthropometrics”.
Late 1800 AC “Edward Henry “ system for indexing fingerprints is introduced in Police Dept, Bengal, India.
mid 1900 AC Fast emergence of biometric systems and applications.

5. Historical Perspective (2)
1870 AC “Bertillon anthropometrics”.
1896 “Henry system” for fingerprint classification.
1936 Iris “biometric concept” is proposed
1960 Face semi-automated recognition is introduced.
1965 Automated signature verification
1969 FBI – automated fingerprint Recognition
First commercial hand-geometry system
1976 First prototype for speech recognition.
1986 Fingerprint minutiae standard published.
1987 First Iris Patent
1991 Real time face detection and recognition
1992 “Biometrics Consortium” established
1993 Face Recognition (FERET) program starts
1995 Iris prototype becomes commercial
1997 FBI CODIS (DNA Forensic database) is created
2000 West Virginia Univ. introduces first biometric program
2003 European Biometrics Forum is formred
…………. (

6. Biometrics: Required or desired properties
Universality: Each person should possess that biometric
Uniqueness (distinctiveness): Different persons should have different biometric characteristics
Permanence: Stability over time (invariance)
Collectability: measurable with enough precision
Acceptability: Compliant to ethical, cultural and moral issues
Other: Accuracy, speed , reliability
FAR: False accept rate
Probability that measurements from two different persons are perceived as belonging to the same person
FRR: False reject rate
Probability that measurements from the same person are perceived as belonging to different persons
ROC: Receiver operating characteristic
A plot of FAR vs FRR for various decision thresholds.

7. Biometrics Comparison Table
VER ID
Accuracy
Reliability
EER
False Pos.
False Neg.
Security
Stability
Acceptance
Intrussive
Usage
(ease)
Cost
Standards
Fingerprint Y 4 3
10-3
Extr.
Diff. 2
Med. L
Face N
Easy
Hand
Geometry
Very H
Speech 1
10-2
Med
Iris
10-5
Retina
10-7
Signature
Keystroke
DNA

8. Generic structure

9. Example

10. Performance and Errors
: The function that measures
the similarity between

11. Receiver Operating Characteristics (ROC) curve

12. Identification vs Verification errors
Let FAR and FRR be the error rates of a biometric verification
System. If this system is utilized as an identification system
involving N identities, then the corresponding rates become:
In other words, for large N, a very small FAR (that is a high
verification rate) is necessary so that the identification system
will not produce a large number of false alarms.

13. More on FAR and FRR
How does a biometric system with a FAR of compare to a 4-digit PIN number ?
A salesman tries to sell you a biometric authentication system with FAR of % and FRR of %. Would you buy this system?
A company with 10,000 employees wants to secure access to a lab facility . Do you think that a biometric solution with FAR = and FRR=0.001 will be appropriate?

14. Biometrics and Privacy
Measure of Privacy:
To what degree identity may be impacted from the use of a biometric system?
How much and what biometric information is personally identifiable?
To what degree biometric data or transactions are linked to each other?
Which biometric data elements are implicitly or explicitly vissible?
The big brother fear: While primarily developed for security, biometrics will be used to track, label and control individuals and work forces.

16. Claims and Pitfalls Biometrics is 100% security
Biometrics apply to everyone
Biometrics are not revocable.
Biometric features can be reconstructed from the template
Biometrics from stolen body parts can be success fully used
Biometrics can be used for medical diagnosis

17. Biometrics is 100% security!!!
Absolute security does not exist: given funding, will, and the proper technology, nearly any security system can be compromised.
Biometrics is only a small part of the security system, and its only ambition is to replace a password or a key, but not the logon software or the door containing the lock.
Moreover, biometrics is not 100% secure. It would say that you are able to give exactly the same biometric sample at each presentation. EvenDNA does not allow 100%, because of the technology (not all bases are sequenced), but also because of twins...

18. Biometrics apply to everyone !!!
In general, 99.9% of the population is able to use one biometric type, but not 100%. Disabilities, habits, and physiological and environmental changes cause problems.
DNA is perhaps the only biometric usable by everyone. Still, twins is a problem in this case.

19. Biometrics are not revocable.
Biometrics are different from a smart card or a token: if a biometric trait is compromised, it is impossible revoke it.
However, note the difference between the "biometric trait“ n(e.g., your physical fingerprint) its electronic version, called biometric signature or biometric template
Biometric traits may be public, (e.g. face for instance). So a security system cannot rely only on biometrics.
The biometric trait cannot change, but the biometric signature can and should be ciphered or encrypted. Then it is possible to revoke it.

20. Biometric features can be reconstructed from the template
In general, it is extremely difficult to generate the biometric trait from the biometric signature. This process is designed to be non-invertible.
It is however possible to spoof a biometric system if you are able to generate a false biometric trait which however, produces a “similarity score” or “matching score” within the authentication range of the original biometric trait

21. Biometrics can be used for medical diagnosis
In the 1995 futuristic movie “Johnny Mnemonic”, Keanu Reeves portrays a data courier who arrives at New York airport and in order to clear security undergoes a full body scan for biometric identification. As his identity is established his vital signs are also monitored and compared to records of his vital signs from the previous visit. A synthesized, “friendly” voice welcomes him to the city and prompts him to seek medical attention as some worrying changes in his vital signs have been detected. Although, today there is no system that can support the above scenario, the latest research in biometric identification, such as gait and heartbeat recognition, provides evidence that such systems are likely to be implemented in the near future.

22. A funny touch!!!

23. Common representational format
Success of recognition in a biometric system depends on
Data representation
matching
Conversion of biometric sensor samples to a common format is a basic requirement for biometric systems – makes observations compatible.
Spatial alignment-common coordinate system
Temporal alignment-common time axis
Sensor reading normalization-common scale
Dimensionality reduction

24. Dimensionality reduction
Biometric data is usually high dimensional-make problem more manageable
Reduce computational load and storage requirements
Enhance discriminative information and therefore classification
Popular approach-subspace methods
SVD, PCA, LDA, ICA

25. Biometric system: Example on Representation , Matching , and Decision
Question: How efficient and discriminative is this representation?

26. Representation (2)
Note: A more efficient representation does not necessarily mean
better discriminating ability!!

27. Representation (3)

28. Matching and decision(1)

29. Matching and decision (2)

33. Principal Component Analysis (PCA) –(1)

34. PCA (2)

35. PCA (3)

36. PCA (4)

37. PCA(5)

38. Linear Discriminant Analysis (LDA)-(1)

39. LDA (2)

40. LDA (3)

41. LDA(4)

42. LDA(5)

43. Subspace methods-classification and eigendirections
A better representation does not necessarily
imply better discrimination ability.
From: “Neural and Adaptive Systems”, J.C. Principe et al., Wiley, 2000.

44. Reading assignments
Anil K. Jain, Arun Ross and Salil Prabhakar, “An Introduction to Biometric Recognition”, IEEE Trans. On Circuits and Systems for Video Technology, Vol.14(1), pp.4-19, January 2004.
Hohn D. Woodward, “Biometrics: Privacy’s Foe or Privacy’s friend?”, Proceedings of the IEEE, Vol. 85(9), pp , Sept. 1997