Neural network method for detecting fuzzy images of faces

V. G. Spitsyn, Yu V. Savitsky, A. B. Kaziev, Yu A. Bolotova

Research output: Contribution to conferencePaper

Abstract

A method for estimating the degree of blurring of an image of a person based on the apparatus of convolutional neural networks is proposed. The possibility of using the original image, the modular component of the frequency spectrum and the phase componen t of the frequency spectrum of the original image as input to the neural network is investigated. The method was tested on an own image base of faces obtained from an IP camera in real conditions. The proposed method is compared with the known method of estimating the blur based on a quantitative analysis of the modular components of the frequency spectrum of the image. On the collected test sample, the proposed method showed the recognition accuracy of 98.57%, the method based on the quantitative analysis of the modular components of the frequency spectrum showed an accuracy of 77.12%.

Original languageEnglish
Pages238-241
Number of pages4
Publication statusPublished - 2018
Event28th International Conference on Computer Graphics and Vision, GraphiCon 2018 - Tomsk, Russian Federation
Duration: 24 Sep 201827 Sep 2018

Conference

Conference28th International Conference on Computer Graphics and Vision, GraphiCon 2018
CountryRussian Federation
CityTomsk
Period24.9.1827.9.18

Keywords

  • Convolutional neural networks
  • Fourier transform
  • Image quality estimation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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  • Cite this

    Spitsyn, V. G., Savitsky, Y. V., Kaziev, A. B., & Bolotova, Y. A. (2018). Neural network method for detecting fuzzy images of faces. 238-241. Paper presented at 28th International Conference on Computer Graphics and Vision, GraphiCon 2018, Tomsk, Russian Federation.