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
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.
featureless comparison, clustering, one-dimensional mapping, neuron, Kullback-Leibler distance, image.
|The work was funded under the government contract Science.
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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
\by V.~B.~Nemirovskiy, A.~K.~Stoyanov, D.~S.~Goremykina
\paper Face recognition based on the proximity measure clustering
\jour Computer Optics
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