Collecting Multimodal Biometric Data Ross J. Micheals Image Group, Charlie Wilson, Manager...
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Transcript of Collecting Multimodal Biometric Data Ross J. Micheals Image Group, Charlie Wilson, Manager...
Collecting Multimodal Biometric Data
Ross J. Micheals
Image Group, Charlie Wilson, ManagerInformation Access Division, Martin Herman, Chief
National Institute of Standards and Technology
International Meeting of Biometrics Experts23 March 2004
The United States government has no multimodal database of
face, fingerprint, and iris images suitable for evaluation.
Challenges
Multimodal Biometrics
Initial motivation: Collect an iris image database
Data collections have substantial fixed costs
Additional sensors are relatively less expensive
Extension of original goal: Collect a multimodal biometric database
Iris Recognition
Iris images are an ICAO (International Civil Aviation Organization) approved biometric
Large market expansion anticipated in early 2005 at expiration of iris recognition concept patent
Iris recognition systems have been deployed internationally and are in operation today
Multimodal Biometrics
There are inherent correlations among different biometric modalities
NIST Face Recognition Vendor Test•Young females (face vs. fingerprints)•Chinese (face vs. iris?)•More data is an opportunity to
discover additional relationships Multimodal data is being collected
right now, every day (US-VISIT)
MBARKMultimodal Biometric Accuracy Research Kiosk
MBARK is an externally deployable, multimodal biometric acquisition and information system
NIST as the maintainer, and synchronizer, and gatekeeper
Two major purposes:• To collect biometric data• To obtain data about collecting biometrics
Multi-agency project• Department of Homeland Security (S&T, TSA)• Intelligence Technology Innovation Center (ITIC)• Department of State
MBARKMultimodal Biometric Accuracy Research Kiosk
Current goal for one MBARK session• Eighteen face images (two sets of nine each)• Forty fingerprints (two sets on two sensors)• Four iris images (two sets of two each)
Most of the data will be sequestered for use in future evaluations
Small portions of the data will be released for scientific and research purposes
Aside:
Privacy Rule of thumb
“Would we want to be in the database?” Suppose we release face, fingerprint, and
iris images of a subject in the database Critical to ensure that multiple modalities
could not be synchronized outside of NIST and Privacy Act protection
Conclusion: Release one and only one modality per subject externally
Research & Operational Needs
Data collections should address a real operational need or a specific research question
Data collected to evaluate a deployed system would be an operational motivation
The design of MBARK reflects a mixture of operational and research needs
MBARK• Face: Operational and research• Fingerprint & Iris: Operational
MBARK : Face
Nine color cameras Five-megapixels per
image Olympus C5050Z Some reliability problems Operational
• Multiple images
Research• Multiple images (FRVT
2002)• Texture-based • Image-based 3D
MBARK: Fingerprint
Optical slap scanners Smiths-Heimann LS2 CrossMatch ID500 Operational
• Ohio WebCheck• Sensor comparisons
MBARK: Iris
Oki IrisPass-WG Near infrared illumination Grayscale iris images Two irises in one sitting User does not need to
manipulate camera Primarily an operational
driven component
MBARK: Registration
Identification of subjects returning later
Using a well-studied model (US-VISIT) as an aid to identify subjects on return visits
Single fingerprint scanner
CrossMatch Verifier 300
Open Systems
NIST evaluations are typically with an emphasis on open systems
Ensures interoperability among components Prevents deployments from being locked
into any particular vendor Requires component evaluations Example: Face Recognition Vendor Test
(FRVT) and Fingerprint Vendor Technology Evaluation (FpVTE) compared algorithms over a set of common images
System vs. Component Evaluation
Iris-recognition market is system oriented
I.e., what you buy is meant to be used in an end-to-end system, rather than an interoperable component
How does this effect image-based evaluations?
Hypothetical example:• “MH Electrics,” iris camera manufacturer• “EyeRidian,” iris recognition software
Iris CameraModel MH 5000
Iris Recognition AlgorithmEyeRidian v1.2
Control Software
Iris Recognition System
Iris Recognition AlgorithmEyeRidian v1.2
Iris Recognition AlgorithmEyeRidian v1.2
Recognition Image Quality
For “high” quality images, recognition is 99.99%
But, suppose only 70% of all data is high quality.
Iris Recognition System
Iris Recognition AlgorithmEyeRidian v1.2
Iris Recognition AlgorithmEyeRidian v1.2
Recognition Image Quality
Iris Recognition System
Iris Recognition System
Iris CameraModel MH 5000
Iris Recognition AlgorithmEyeRidian v1.2
Image QualityMH ControlSoftware
What about the 30% of images that are not “high” quality?
How might other algorithms do on these images?
If there are no images of sufficient quality, the sensor reports a failure to acquire (FTA).
FTA data is usually not available for image-based evaluations.
Iris CameraModel MH 5000
Iris Recognition AlgorithmEyeRidian v1.2
Image QualityMH ControlSoftware
Iris Recognition System
Conclusion
In component testing, be aware of the internals of each component and how evaluations might be effected
For some modalities, we can reduce bias by using a mix of sensors• Example: Many fingerprint scanners all with
different control logic
For other modalities, testing components requires more sensitivity
The degree of this minimization depends on the state of the market and vendor support
Questions?