Section 2.3.5 – Biometrics
1
Biometrics
• Biometric refers to any measure used to uniquely identify a person based on biological or physiological traits.
• Generally, biometric systems incorporate some sort of sensor or scanner to read in biometric information and then compare this information to stored templates of accepted users before granting access.
2Image from http://commons.wikimedia.org/wiki/File:Fingerprint_scanner_in_Tel_Aviv.jpg used with permission under the Creative Commons Attribution 3.0 Unported license
Requirements for Biometric Identification
• Universality. Almost every person should have this characteristic.
• Distinctiveness. Each person should have noticeable differences in the characteristic.
• Permanence. The characteristic should not change significantly over time.
• Collectability. The characteristic should have the ability to be effectively determined and quantified.
3
Biometric Identification
4
Feature vector
Reference vector
Comparison algorithm
matches doesn’t match
BiometricReader
CIT 380: Securing Computer Systems
Slide #5
Biometric Measurement
Possible Outcomes:
1. Correct person accepted2. Imposter rejected3. Correct person rejected (False Rejection)4. Imposter accepted (False Acceptance)
CIT 380: Securing Computer Systems
Slide #6
False Positives and NegativesTradeoff between
• False Accept Rate• False Reject Rate• Crossover Error Rate
Candidates for Biometric IDs
• Fingerprints• Retinal/iris scans• DNA• “Blue-ink” signature• Voice recognition• Face recognition• Gait recognition• Let us consider how each of these scores in terms of
universality, distinctiveness, permanence, and collectability…
7
Public domain image from http://commons.wikimedia.org/wiki/File:Retinal_scan_securimetrics.jpg
Public domain image from http://commons.wikimedia.org/wiki/File:CBP_chemist_reads_a_DNA_profile.jpg
Public domain image from http://commons.wikimedia.org/wiki/File:Fingerprint_Arch.jpg
CIT 380: Securing Computer Systems
Slide #8
FingerprintsCapacitive measurement, using differences in electrical charges of whorls on finger to detect those parts touching chip and those raised.
CIT 380: Securing Computer Systems
Slide #9
Brandon Mayfield
• Fingerprints found in 2004 Madrid bombing.• Brandon arrested May 6, 2004.• FBI claimed “100 percent positive” match.
– Held under a false name.– Then transferred to unidentified location.
• Spanish police identify fingerprint as belonging to an Algerian man May 21, 2004.
• Brandon released May 25, 2004.
CIT 380: Securing Computer SystemsSlide #10
Eye Biometrics• Iris Scan
– Lowest false accept/reject rates of any biometric.
– Person must hold head still and look into camera.
• Retinal Scan– Cataracts and pregnancy change
retina pattern.– Lower false accept/reject rates
than fingerprints.– Intrusive and slow.
CIT 380: Securing Computer Systems
Slide #11
Other Types of Biometrics
Physiological
• DNA• Face recognition• Hand geometric• Scent detection• Voice recognition
Behavioral
• Gait recognition• Keyboard dynamics• Mouse dynamics• Signatures
CIT 380: Securing Computer Systems
Slide #12
Biometrics are not infallibleWhat are False Accept and Reject Rates?Do the characteristics change over time?
– Retina changes during pregnancy.– Fingerprint damage due to work/pipe smoking.– Young and old people have fainter fingerprints.
Is it accurate in the installed environment?– Is someone observing fingerprint or voiceprint checks?– i.e., did you collect biometric from the person?
CIT 380: Securing Computer Systems
Slide #13
Biometrics can be compromised.
Unique identifiers, not secrets.– You can change a password.– You can’t change your iris scan.
Examples:– You leave your fingerprints every place.– It’s easy to take a picture of your face.
Other compromises.– Use faux ATM-style devices to collect biometrics.– Obtain all biometric templates from server.
CIT 380: Securing Computer Systems
Slide #14
Use and Misuse of Biometrics Employee identification.
– Employee enters login name.– System uses fingerprint to verify employee is who he
claims to be.– Problem: Does biometric match the employee?
Criminal search (Superbowl 2001)– System uses face recognition to search for criminals in
public places.– Problem: Does any biometric in database match anyone in
a crowd of people?– Assume system is 99.99% accurate and 1 in 10million
people is a terrorist. Result: 1000 false positives for each terrorist.