Please use this identifier to cite or link to this item:
http://earchive.tpu.ru/handle/11683/36151
Title: | Face recognition based on the proximity measure clustering |
Authors: | Nemirovskiy, Viktor Borisovich Stoyanov, Aleksandr Kirillovich Goremykina, Darjya Sergeevna |
Keywords: | кластеризация; картография; нейроны; расстояние Кульбака-Лейблера; распознавание лиц |
Issue Date: | 2016 |
Publisher: | Томский политехнический университет |
Citation: | Nemirovskiy V. B. Face recognition based on the proximity measure clustering / V. B. Nemirovskiy, A. K. Stoyanov, D. S. Goremykina // Компьютерная оптика. — 2016. — Т. 40, № 5. — [P. 740-745]. |
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. |
URI: | http://earchive.tpu.ru/handle/11683/36151 |
Appears in Collections: | Репринты научных публикаций |
Files in This Item:
File | Description | Size | Format | |
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reprint-nw-17764.pdf | 201,77 kB | Adobe PDF | View/Open |
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