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Эта публикация цитируется в 10 научных статьях (всего в 10 статьях)
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
Аннотация:
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.
Ключевые слова:
featureless comparison, clustering, one-dimensional mapping, neuron, Kullback-Leibler distance, image.
Поступила в редакцию: 14.05.2016 Принята в печать: 18.06.2016
Образец цитирования:
V. B. Nemirovskiy, A. K. Stoyanov, D. S. Goremykina, “Face recognition based on the proximity measure clustering”, Компьютерная оптика, 40:5 (2016), 740–745
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/co295 https://www.mathnet.ru/rus/co/v40/i5/p740
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