Face recognition based on the proximity measure clustering
Автор: Nemirovskiy Victor Borisovich, Stoyanov Alexander Kirillovich, Goremykina Darya Sergeevna
Журнал: Компьютерная оптика @computer-optics
Рубрика: Обработка изображений: Распознавание образов
Статья в выпуске: 5 т.40, 2016 года.
Бесплатный доступ
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
Короткий адрес: https://sciup.org/14059613
IDR: 14059613 | DOI: 10.18287/2412-6179-2016-40-5-740-745
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