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sulting networks exhibit certain traits that mimic human intelligence For instance, they can generalize well, allowing them to match against incomplete data or data with naturally occurring variations Neuron models were first developed in the 1950s and 1960s While many individuals contributed to their development, Marvin Minsky is most notably associated with the first generation of neural networks and, interestingly, also in subsequently establishing some of their shortcomings (Minsky 1969) As a computing paradigm, there is an enormous range of processing architectures that can be called neural networks Many details in the feature selections, weightings, and error correction schemes have led to many variations of the basic theme, each with subtle differences in behavior or benefits for certain data While the exploration has enriched the study of neural networks, the diversity also means that different vendor implementations may produce different results from the same data And that is a potential drawback for some security applications Eigenfaces is the term used to categorize a second broad class of algorithms that represent and compare faces on the basis of a palette of facial abstraction images The development of this technique is associated with Matthew Turk and Alex Pentland, who demonstrated a process for how facial abstraction images, or characteristic eigenfaces, are generated from a collection of images, and then the faces are expressed as a weighted sum of these archetypal faces (Pentland 1991, 71 86) The desired similarity or likeness between faces can then be expressed as a numerical distance on the basis of these weights It has been argued that this classification technique bears no semblance to the way humans recognize and gauge similarity between faces Nonetheless, the mathematical properties of the eigenface representation and matching process has been demonstrated to achieve reasonable results in certain minimally controlled environments Local feature analysis refers to a class of algorithms that extract a set of geometrical metrics and distances from facial images and uses those features as the basis for representation and comparison (note the local features could be used in conjunction with a neural network) The actual features used in today s commercial products are considered proprietary by the companies and are not made public Instead they are described only in general terms, and are the things we might expect: the mouth, nose, jaw line, eyebrows, and checks Local feature analysis must locate and extract these features, and represent their position, size, and general outline shape3 Faces can then be compared on the basis of their similarity to their ingredient features In general, the local feature analysis technique is attractive because it represents faces as less abstract, vector-based features; and the technique has demonstrated that it is one of the better performing tech3
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P Penev, J Atick, 1996, Local Feature Analysis: A General Statistical Theory for Object Representation, Network: Computation in Neural Systems, vol 7, 477-500 See also T Choudhury, 2000 Current State of the Art, http://www-whitemediamitedu/tech-reports/ TR-516/node8html
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niques However, local feature analysis may not generalize as well across environments as other techniques because it depends on successful and uniform quality for the location and extraction of all the primary features Attempts to refine this technique include adjusting the weight of the subcomparisons according to human cognition studies Alternatively, more pragmatic approaches to optimize performance might attempt to set the weights according to statistical properties of the features as they exist in the enrolled population There is no single error rate for the technology, as performance depends on the environmental factors and the data presented However, government testing has illustrated the detailed effect of these factors on performance and shown that technology vendors are capable of delivering 75 80 percent accuracy under certain simulated operational conditions, and higher (90 98 percent) under ideal conditions4 Nonetheless, critics question the effectiveness of the technology in real-world circumstances The urgency of recently piloted field tests (in airports) has drawn attention and raised expectations, often resulting in hasty, incomplete reporting and bogus conclusions Some of the recent trial deployments failed to put forth the time and effort necessary to properly calibrate the technology into the target environment And none of the tests seriously considered integrating the technology in with other systems such as travel documents to both boost performance and, more importantly, to help disambiguate the alarms that can and should be predicted to occur Well-integrated and well-calibrated systems, even if they involve some manual control and review, have found the accuracy of facial recognition to be sufficient for casinos to put the technology to use since the late 1990s as a means to help spot and track banned players There are image standards that address minimal image quality and desired orientation of mug shots for facial biometrics The resolution of the database and probe images, measured by the number of pixels across the face or between the eyes, should adhere to these standards if reasonable results are expected Also, consistent quality for image acquisition and storage is desirable and should be maintained across the application5 As discussed earlier, there currently are no standards for what face features should be used, how they should be weighted for comparison, or how they can be stored in a generalized format conducive to interchange and interoperability All the details of the image processing, feature selection, isolation, extraction, and representation are proprietary and vendor specific As a result, there is a range of performance and specialization present in today s commercial face recognition applications
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P J Phillips, et al, 2000, FRVT 2000 Evaluation Report Department of Defense Counterdrug Technology Development Program Office, http://wwwfrvtorg/FRVT2000/documentshtm National Institute of Standards and Technology, 2000, Data Format for the Exchange of Fingerprint, Facial, and Scars Marks and Tattoo (SMT) Information, ANSI/NISTCSL 1a-2000 (amendment)
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