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Computer Optics, 2016, Volume 40, Issue 5, Pages 740–745 (Mi co295)  

IMAGE PROCESSING, PATTERN RECOGNITION

Face recognition based on the proximity measure clustering

V. B. Nemirovskiy, A. K. Stoyanov, D. S. Goremykina

Institute of Cybernetics of Tomsk Polytechnic University, Tomsk, Russia

Abstract: In this paper problems of featureless face recognition are considered. The recognition is based on clustering the proximity measures between the distributions of brightness clusters cardinality for segmented images. As a proximity measure three types of distances are used in this work: the Euclidean, cosine and Kullback-Leibler distances. Image segmentation and proximity measure clustering are carried out by means of a software model of the recurrent neural network. Results of the experimental studies of the proposed approach are presented.

Keywords: featureless comparison, clustering, one-dimensional mapping, neuron, Kullback-Leibler distance, image.

Funding Agency
The work was funded under the government contract “Science”.


DOI: https://doi.org/10.18287/2412-6179-2016-40-5-740-745

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Full text: http://www.computeroptics.smr.ru/.../400517.html
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Received: 14.05.2016
Accepted:18.06.2016
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Citation: V. B. Nemirovskiy, A. K. Stoyanov, D. S. Goremykina, “Face recognition based on the proximity measure clustering”, Computer Optics, 40:5 (2016), 740–745

Citation in format AMSBIB
\Bibitem{NemStoćīš16}
\by V.~B.~Nemirovskiy, A.~K.~Stoyanov, D.~S.~Goremykina
\paper Face recognition based on the proximity measure clustering
\jour Computer Optics
\yr 2016
\vol 40
\issue 5
\pages 740--745
\mathnet{http://mi.mathnet.ru/co295}
\crossref{https://doi.org/10.18287/2412-6179-2016-40-5-740-745}


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