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

Full text of article: English language.

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.

Citation:
Nemirovskiy VB, Stoyanov AK, Goremykina DS. Face recognition based on the proximity measure clustering. Computer Optics 2016; 40(5): 740-745. DOI: 10.18287/2412-6179-2016-40-5-740-745.

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