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The computation of any similarity score will be precluded by missing values. Missing values can be attributed to poor quality biometric data, poor data capture, or classifier error from computing the similarity scores. The presence of missing values in biometric systems can be inconvenient to the user, as the system will reject the submitted biometric data and request for another. It is therefore...
It has been shown that the authentication performance of a biometric system is dependent on the models/templates specific to a user. As a result, some users may be more easily recognized or impersonated than others. The various categories of users have been characterized by Doddington etal. (1988). We refer to this unbalanced performance across users as the Doddington's zoo effect. In the context...
In this paper we apply state-of-the-art approach to object detection and localisation by incorporating local descriptors and their spatial configuration into a generative probability model. In contrast to the recent semi- supervised methods we do not utilise interest point detectors, but apply a supervised approach where local image features (landmarks) are annotated in a training set and therefore...
In this paper, we derive a data association algorithm for object tracking in a maximum a posteriori framework: the output of the algorithm is the sequence of measurement-to-target associations with maximum a posteriori probability. We model the object motion as a Markov process, and solve this otherwise combinatorially complex problem efficiently by applying the Viterbi algorithm. A method for combining...
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