We report the development of a face recognition system which operates in the same way as humans in
that it is capable of recognizing a number of people, while rejecting everybody else as strangers. While
humans do it routinely, a particularly challenging aspect of the problem of open-world face recognition
has been the question of rejecting previously unseen faces as unfamiliar. Our approach can handle
previously unseen faces; it is based on identifying and enclosing the region(s) in the human face space
which belong to the target person(s).
Results are described of an ongoing project whose goal is to provide advanced Computer Vision for small low flying autonomous aircraft. The work consists of two parts; range-based vision for object recognition and pose estimation, and monocular vision for navigation and collision avoidance. A wide variety of range imaging methods were considered for the former, and it was found that a promising approach is multi-ocular stereo with a pseudo-random texture projected with a xenon flash. This provides high range resolution despite motion, and can be small and light. The resulting range images, taken at a few meters range, would support the use of Tripod Operators, an efficient and general method for recognizing and localizing surface shapes in 6 DOF. This would provide the ability to recognize immediately upon encounter many kinds of targets. The monocular navigation system is based on finding corresponding features in successive images, and deducing from these the relative pose of the aircraft. Two methods are under development, based on horizon registration and point correspondences, respectively. The first can serve as a preprocessor for the second. This approach aims to continuously and accurately estimate the net motion of the vehicle.
In target recognition in uncontrolled environments the test target may not belong to the prestored targets or target classes. Hence, in such environments the use of a typical classifier which finds the closest class still leaves open the question of whether the test target truly belongs to that class. To decide whether a test target matches a stored target, common approaches calculate a degree of similarity between the two targets using a similarity measure such as Euclidean distance, and make a decision based on whether the distance exceeds a (prespecified) threshold. Based on psychophysical studies, this is very different from, and far inferior to, human capabilities. In this paper we show a new approach where a neural network learns a decision boundary between the confirmation vs. rejection of a match with the help of a human critic. The decision boundary is a multidimensional surface, and models the human similarity measure for the recognition task at hand, thus avoiding metric similarity measures and thresholds. A case study in automatic aircraft recognition is shown. In the absence of sufficient real data, the approach allows us to specifically generate an arbitrarily large number of training exemplars projecting near the classification boundary. The performance of the trained network was comparable to that of a human expert, and far better than a network trained only on the available real data. Furthermore, the result were considerably better than those obtained using a Euclidean discriminator.
Efforts are underway to develop the capability for small unmanned underwater vehicles to use the Earth's gravitational field for autonomous navigation. A main aspect of navigation is vehicle localization on an existing gravity map. We have developed machine vision-like algorithms that match the onboard gravimeter measurements to the map values. In gravity maps there are typically a dearth of distinctive topographic features such as peaks, ridges, ravines, etc. Moreover, because the gravity field can only be measured in-place, probing for such features is infeasible as it would require extensive surveys. These factors, make the commonly used feature matching approach impractical. The localization algorithms we have developed are based on matching with contours of constant field value. These algorithms are tested on simulated data with encouraging results. Although these algorithms are developed for underwater navigation using gravity maps, they are equally applicable to other domains, for example vehicle localization on an existing terrain map.
Most of the pattern recognition applications of multilayer neural networks have been concerned with classification and not rejection of a given pattern. For example, in character recognition all alphabetical characters must be recognized as one of the 26 characters, as there is nothing to reject. However, in many situations, there is no guarantee that all the patterns that will be presented to the network would actually belong to one of the classes on which the network has been trained. In such cases, a useful network must be capable of rejection as well as classification. In this paper we propose a scheme to develop multilayer networks with rejection capabilities. The discriminating power of the proposed technique appears to be comparable to that of the human eye.
Conference Committee Involvement (5)
Automatic Target Recognition XVIII
19 March 2008 | Orlando, Florida, United States
Automatic Target Recognition XVII
10 April 2007 | Orlando, Florida, United States
Biometric Technology for Human Identification III
17 April 2006 | Orlando (Kissimmee), Florida, United States
Biometric Technology for Human Identification II
28 March 2005 | Orlando, Florida, United States
Optics and Photonics in Global Homeland Security 2003
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