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This article is cited in 10 scientific papers (total in 10 papers)
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.
Received: 14.05.2016 Accepted: 18.06.2016
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
Linking options:
https://www.mathnet.ru/eng/co295 https://www.mathnet.ru/eng/co/v40/i5/p740
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Abstract page: | 129 | Full-text PDF : | 49 | References: | 26 |
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