Guide to Biometric Reference Systems and Performance Evaluation
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The development of biometric systems is one of the labor-intensive processes. Therefore, the creation and analysis of approaches and techniques is an urgent task at present.
Guide to Biometric Reference Systems and Performance Evaluation
This article presents a technique of modeling and prototyping biometric systems based on dataflow programming. The technique includes three main stages: the development of functional blocks, the creation of a dataflow graph and the generation of a prototype. A specially developed software modeling environment that implements this technique is described. As an example of the use of this technique, an example of the implementation of the iris localization subsystem is demonstrated. A variant of modification of dataflow programming is suggested to solve the problem related to the undefined order of block activation.
The main advantage of the presented technique is the ability to visually display and design the model of the biometric system, the rapid creation of a working prototype and the reuse of the previously developed functional blocks. Content from this work may be used under the terms of the Creative Commons Attribution 3. Any further distribution of this work must maintain attribution to the author s and the title of the work, journal citation and DOI.
Even in DNA analysis, there has been controversy and uncertainty over how to estimate distinctiveness. Some biometric traits, such as fingerprints, appear to be reasonably stable, but others, such as facial characteristics, can change significantly over even short periods of time. Depending on the capture and matching algorithms, changes in a trait over time may or may not have an effect on system performance and whether that person is appropriately recognized.
Understanding more about the stability of common biometric traits will be important, especially if biometric systems are deployed for comparatively long years or decades periods of time. All of this suggests several avenues of research that could strengthen the scientific underpinnings of the technology. There needs to be empirical analysis of base-level distinctiveness and the stability of common biometric modalities, both absolutely and under common conditions of capture, and research into what types of capture and what models and algorithms produce the most distinguishable and stable references for given modalities.
Further, the scalability of various modalities under different capture and modeling conditions must be studied. Every biometric system relies on one or more biometric modalities. The choice of modality is a key driver of how the system is architected, how it is presented to the user, and how match vs. Understanding particular modalities and how best to use the modalities is critical to overall system effectiveness. Research into several interrelated areas will bring continued improvement:.
In addition to the general challenges described above, there are also challenges specific to particular biometric modalities and traits. While the following discussion does not describe all the challenges for each modality, it does offer some potentially fruitful avenues of investigation for the most common ones.
An ongoing challenge for facial recognition is segmentation—distinguishing facial features from surrounding information. Another signifi cant challenge for it is invariant representation—that is to say, finding a representation that is robust and persistent even when there are changes in pose, expression, illumination, and imaging distance, or when time has passed. Specific challenges with respect to fingerprints include reducing the failure to enroll FTE and failure to acquire FTA rate, perhaps through the design of new sensors, artifact detection, image quality definition and enhancement, and high-resolution fingerprint matching.
Fingerprint-based biometric systems could also be improved by increasing the speed of capture and minimizing contact, particularly for print systems. Improving speaker separation, normalizing channels, and using higher-level information that is, beyond basic acoustic patterns would all offer opportunities to improve voice recognition. In addition, robustness and persistence are needed in the face of language and behavioral changes and the limited number of speech samples. In many applications, biometric systems are one component of an overarching security policy and architecture. The information security community is extensive and has long experience with some of the challenges raised by biometric systems, which gives it a real opportunity for fruitful and constructive interaction with the biometrics community.
Biometric systems pose two kinds of security challenges. The first is the use of biometrics to protect—provide security for—information systems. For what types of applications and in which domains is an approach incorporating biometric technologies most appropriate? This is a question for the broader information security community as well as the biometrics community and requires that we understand the goals and needs of an application to ascertain whether a biometrics-based approach is useful. Assuming that a biometrics system is in place, the second security challenge is the security, integrity, and reliability of the system itself.
Developing techniques for protecting biometric reference information databases to avoid their use as a source of fake biometrics is another area for such research. Decision analysis and threat modeling are other critical areas requiring research advances that will allow employing biometric systems more fully across a range of applications. Testing and evaluation are an important component in the design, development, and deployment of biometric systems.
Several areas related to the testing and evaluation of biometric systems are likely to prove fruitful. This section describes a few of them. Moreover, while standardized evaluations of biometric systems are highly useful for development and comparison, their results may not reliably predict field performance.
Methods used successfully for the study and improvement of systems in other fields for example, controlled observation and experimentation on operational systems guided by scientific principles and statistical design and monitoring should be used in developing, maintaining, assessing, and improving biometric systems. See Chapter 3 for lessons that may be applicable from other domains. The work over the last decade within the international standards community to reach agreement on fundamental concepts, such as how error rates are to be measured, has clarified the application of test methods under the usual laboratory conditions for biometric systems deployments.
Unfortunately, ROI analysis methodologies and case studies have been lacking in comparison to other types of assessments. See Box 5. Return on Investment and Suitability Considerations. Determining the return on investment ROI for a biometric system is very much dependent on the application. It is based, among other things, on the risk the system is mitigating, the severity of the more Ultimately, determining the performance of an operational system requires an operational test, because adequately modeling all of the factors that impact human and technology performance in the laboratory is extremely difficult.
Careful process and quality control analysis—as distinct from traditional, standardized testing of biometric systems that focuses on match performance for a test data set—at all stages of the system life cycle is essential. In addition, testing methods and results should be sufficiently open to allow disinterested parties to assess the results. One challenge meriting attention is test data for biometric systems. Designing large-scale systems requires large test data sets that are representative of the subject population, the collection environment, and system hardware expected in the target application.
How does one determine which user population will be representative of the target application? The committee believes it is unlikely that being representative of the target application is the same as being representative of the population as a whole, because the population that should be considered will vary depending upon the ultimate application for which the system is used.
Legal and privacy concerns have limited the collection and sharing of both test and operational data for example, various data sets collected by the U. When test results are available, who has access to them? Many factors related to usability can affect system effectiveness and throughput and may also affect how well the system performs its recognition tasks. Testing and evaluation mechanisms are therefore needed that provide insight into how well a system under consideration handles a variety of user interface expectations.
One potential area of investigation is to incorporate into the design of the interface information on the expected motor control and cognitive capabilities of the user popu lations. Such information would allow the use of public health statistics to estimate the percentage of the general population or subpopulation that would be expected to have either cognitive or physical difficulties using the systems.
By incorporating this understanding of the skills expected of users, designers and developers could tune the interfaces in ways that would increase their usability. Usability is affected by other factors as well. For example, some unknown percentage of the population has a condition in which the fingers do not possess the usual friction ridges central to the functioning of fingerprint-based biometric system. In addition, some unknown but believed to be nonzero percentage of the population has either no irises or irises of unusual shape.
When setting baseline error rates, it is important to have estimates of the percentage of the population lacking the required trait, because this lack interacts with the design of sensors and algorithms. It may well be that each modality will have lower error rate bounds that cannot be improved upon by better sensors, algorithms, or collection procedures. More research is needed to understand this.
Guide to Biometric Reference Systems and Performance Evaluation (Hardcover)
Such questions are related to the distinctiveness and stability of the underlying biometric traits, discussed above. Other usability considerations relate to the ease of participation. Is the system designed to take into account user needs such as tables on which to set their items if necessary and physical differences such as height and weight? What kinds of user assistance should be provided?
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What accommodations could be made for people who are unfamiliar with the system, or, conversely, for people who are very familiar with it much as toll pass transponder users can use dedicated lanes on highways? Determining the potential ROI and identifying which system characteristics contribute is an important means of evaluating any biometrics deployment. In addition to how well a system meets its requirements, there are issues about measuring cost over the life cycle of the system and assessing potential and actual ROI.
There are relatively few ROI analysis methodologies and case studies. The research opportunity here is to develop methods for examining likely costs and cost savings that take into account the technical life cycle as well as ongoing maintenance and usage costs. In addition to system and technology tests, there is a significant opportunity to develop an evaluative model that would guide potential procurers and users of biometric systems. Guidance for potential users of biometric systems on an appropriate initial set of questions to ask before getting into the details of modalities and so forth has proven particularly useful.
Designing a system and tests that can cope with ongoing data collection after it has been deployed is a significant challenge. The characteristics of the data may change from what was assumed during testing. This could be due to changes in the technology, changes in the user population, changes in how the system is used, or all of the above. Finally, operational testing is problematic in that most existing systems do not retain the data needed to determine error and throughput rates.
Each system collects and stores different data in application-specific ways. Additionally, ground truth all of the relevant facts about all participants cannot be known in real applications with arbitrary user populations. Privacy rights of the data subjects may prevent using collected data for testing purposes.
Lastly, because system operators may not wish others to know about operational performance for reasons of security, very few operational test results have ever been published. In addition to the modality-related technical challenges outlined above, there are broad systems-level considerations to take into account.