The writer recognition task has received a lot of interests during the last decade due to it wide range of applications. This task includes writer identification and/or writer verification. However, all the researches...
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The writer recognition task has received a lot of interests during the last decade due to it wide range of applications. This task includes writer identification and/or writer verification. However, all the researches assumed that they dispose of a large amount of text to identify or authenticate the writer, which is never the case in real-life applications. In this paper, we present an original approach for the writer authentication task based on the analysis of a unique sample of a handwriting word. We used the Levenshtein edit distance based on fisher-wagneralgorithm to estimate the cost of transforming one handwritten word into another. Such method has been successfully applied for signature authentication and voice recognition. In order to apply it to handwriting words, we developed a segmentation module to generate the graphemes;considered as elementary components for each word. We evaluated this approach on part of the IAM database (100 writers), where half of them provided three samples only of the same word. The obtained results are very promising since we succeed to accept correctly in 87 % of cases when we used the whole database (100 writers) and up to 92 % when we used 40 writers.
Vibration measurement using coherent laser radar (LADAR) is a promising way to identify air targets at long range. Laser vibrometers can remotely measure the velocity of micrometric displacements and thus exhibit the ...
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ISBN:
(纸本)0819457922
Vibration measurement using coherent laser radar (LADAR) is a promising way to identify air targets at long range. Laser vibrometers can remotely measure the velocity of micrometric displacements and thus exhibit the target surface vibration frequencies. Some of these frequencies are modal frequencies, which result from the target structure. They define a unique signature and allow target identification to be performed. As vibration amplitudes are not reliable, we choose to consider only frequency positions. In this article, we explain an "extended identification" method which takes into account cumulative signatures in space and time to improve global system identification performance. Using a nearest neighbor classifier and a suitable metric taking into account a simple off-line processing of measured data, the recognition algorithm leads to good identification rates and very low rejection rates for a nine class problem. We show a strong improvement of the identification rate thanks to the "extended identification" method.
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