Biometric Recognition Challenges and Opportunities (2010) / Chapter Skim
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2 Engineering Biometric Systems
2 Engineering Biometric Systems
Pages 53-75
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From page 53... ...
A systems engineering view is especially important when the systems are to be used on a large scale, such as for border control or social service entitlement, when all the best practices associated with system design and management are called for. While the evolution of sensor devices, matching technologies, and human factors can dominate the attention of system designers, the ultimate success of the overall system generally relies on attention to conventional system development issues.
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From page 54... ...
• A holistic security model that addresses the application security context and provides a cogent analysis of the potential for fraudulent presentation of biometric samples. Successful biometric applications require top-down conceptualization, with clear delineation of purpose within a systems context.
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From page 55... ...
tionally obtained samples.1 The data samples are then processed to form Figure 2-1 references that are stored for vector, editable future comparison in a database or on trans portable media such as a smart card.2 Depending on the requirements of 1 However, quality assessment algorithms for biometric enrollment are not universally available and may need to be specific to the proprietary feature extraction and comparison algorithms used later in the process. In some applications, such as use of facial recognition with images stored by passport issuance agencies on electronic passports, there will be no knowledge during the enrollment process of what algorithms might be used later to com pare the stored image.
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From page 56... ...
The process of enrolling the subject's biometric data allows for subsequent retrieval of identity attributes. The legitimacy of the subject's association with those attri butes, however, must be established by means outside the biometric enrollment process.
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From page 57... ...
In principle the choice of biometric features, the degree of independent information and hence distinctiveness conveyed by each additional feature, and the balance between feature multiplicity and storage efficiency can have major effects on recognition error rates. This is borne out by the large performance differences exhibited in tests that compare products using the same biometric trait.6 Storage of the reference, whether in the form of samples, features, or models, completes the enrollment process.
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From page 58... ...
Otherwise a nonmatch is declared or possibly, for an intermediate window of scores, an indeterminate result is declared and a cooperative subject may be asked to resubmit the same biometric sample, an alternative biometric sample, or take additional action, such as contacting a security guard to execute a manual fallback procedure. If the desired operation is identification, then the sample features are compared against a portion or all of the reference database.
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From page 59... ...
For any given class of applications or, more precisely, for any target deployment, one can begin an analysis of where on this table the demands TABLE 2.1 Parameters That Affect System Design Decisions and System Effectiveness Parameter Degree or Intensity from High to Low User context Data subject awareness Very Not very Data subject motivated Very Not very Data subject well- Very Not very trained Data subject habituated Very Not very Who benefits? Both User/consumer Owner/agency Application context Application supervised Very Not very Application type Positive claim Negative claim Application type Verification One to few matching Identification Data interoperability Closed Supposed to be closed Open Technology context Environment controlled Very Not very Passive versus active Active Passive w/cooperation Passive Covert versus overt Overt Covert Performance context Throughput Low Medium High requirements Sensitivity to error rate Low Medium High requirements
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From page 60... ...
By contrast, a border control system using biometrics will be faced with users who are not well trained and perhaps not well motivated but will nonetheless have high throughput and stringent error rate requirements and so on. Clearly, stating that a system is a biometric system or uses "biometrics" does not provide much information about what the system is for or how difficult it is to successfully implement.
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Is there an opportunity in the enrollment process to provide feedback on correct or incorrect feature presentation? Many applications may not give data subjects a chance to have human interaction with system staff during enrollment or subsequent uses.
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These systems often require a claim by the data subject to a specific reference and by extension to the corresponding enrollment record. However, alternative examples of positive claim systems, needing the unspecific claim "I am enrolled," have existed since the early 1990s.
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These systems perform a so-called one-toone type of match, usually by the data subject providing a name, number, token, or password that points to or unlocks the subject's enrollment ref erence.9 Identification systems generally scan all references in a database to see if there is a match to the sample presented. Typically, verification systems are positive claim systems;10 identification systems can be either positive or negative claim systems; however, due to the nature of those applications (see discussion of positive vs.
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From page 64... ...
For example, face recognition systems currently being used in immigration control at a number of airports worldwide use enrollment images stored on e-passports. Those images are placed on the passports by passport issuance agencies and are generally based on photographs submitted by the passport applicant.
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From page 65... ...
Voice recognition applications could also be considered passive if they are based on regular conversational speech without subject interaction. Active tech nologies are those that require direct human interaction such as speaking a particular phrase or positioning a finger, hand, or head in the correct location.
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These are typically contribu tions from signal noise and background noise, but typically the largest components are the human interaction and environmental components. Error rates have direct impact on throughput, since false rejections take significantly longer to process than acceptances (whether true or false)
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From page 67... ...
(In criminal justice systems, there is no known change in error rates when each booking site selects whichever certified scanning system it wants.) It has been observed that the matching performance drops when the reference and test samples for fingerprint, iris, and voice are acquired using different sensors rather than the same.13 There are several reasons for this degradation in matching performance: (1)
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From page 68... ...
Although other technology interfaces such as are found in automatic teller machines, automobiles, televisions, and self-service gasoline pumps have a level of standardization that allows transferring experience gained with one system to other systems, little has been done in this area for biometrics, and these mass-market interfaces can confuse even experienced users on occasion. More standardized user interfaces coupled with broader human factors testing would contribute to greater maturity in all biometric applications.14 SYSTEM LIFE-CYCLE ISSUES Biometric systems that are large in scale and that are expected to persist and be used for more than a short period of time face the same challenges as other large-scale technology implementations.15 Software and 14 For more on usability and biometric systems, see "Usability and Biometrics: Ensuring Successful Biometric Systems," available at http://zing.ncsl.nist.gov/biousa/docs/ Usability_and_Biometrics_final2.pdf.
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From page 69... ...
The fingerprint acquisition and/or fingerprint matching software, or the file with the enrolled biometric template -- a software issue -- can also be corrupted. What happens if the system can no longer be used to recognize the user?
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From page 70... ...
A draconian solution to this security prob lem would be to automatically monitor sensor performance, letting sensor failure or replacement initiate a data overwrite or physical destruction of the storage medium or of cryptographic keys used to encrypt storage on the machine. However, this approach could be detrimental to the owner unless exceptionally stringent backup systems were in place.
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Technology testing evaluates feature/model extraction and comparison algorithms using a 16 Seethe NIST National Voluntary Laboratory Accreditation Program (NVLAP) Handbook 150-25 on Biometric Testing, which is available at http://ts.nist.gov/Standards/ Accreditation/upload/NIST-HB150-25-2009.pdf and http://ts.nist.gov/Standards/ Accreditation/bio-lap.cfm.
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From page 72... ...
18 Work along these lines includes efforts undertaken by the British government since 1999, NIST Speaker Recognition Evaluation since 1994, and ISO/IEC JTC1 SC37 since 2002. There is also NIST's emerging National Voluntary Laboratory Accreditation Program (NVLAP)
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From page 73... ...
This could be broadened to include system operators and system administrators, and, in some cases, systems owners as users.20 Test and Evaluation Standards Biometric testing standards have evolved to address various forms of testing. Biometric performance testing and reporting of international standards, published as the ISO/IEC 19795 series of standards, evaluate biometric systems in terms of error rates and throughput rates.21 Metrics for the various error rates in biometric enrollment, verification, and identification are specified.
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From page 74... ...
In this sense, performance encompasses not just error rates but also throughput, reliability, and other features crucial to system success. For example, systems that are engineered so that performance can be dynamically monitored during testing and deployment should offer system administrators performance data throughout the operational life cycle.
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From page 75... ...
Downing, Effect of severe image compression on iris recognition performance, IEEE Transactions on Information Forensics and Security 3:1 (2008)
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