Development of technique for face detection in image based on binarization, scaling and segmentation methods

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.195369

Keywords:

face detection, image, binarization, scaling, segmentation, density clustering

Abstract

A technique for face detection in the image is proposed, which is based on binarization, scaling, and segmentation of the image, followed by the determination of the largest connected component that matches the image of the face.

Modern methods of binarization, scaling, and taxonomic image segmentation have one or more of the following disadvantages: they have a high computational complexity; require the determination of parameter values. Taxonomic image segmentation methods may have additional disadvantages: they do not allow noise and outliers selection; clusters can’t have different shapes and sizes, and their number is fixed.

Due to this, to improve the efficiency of face detection techniques, the methods of binarization, scaling and taxonomic segmentation needs to be improved.

A binarization method is proposed, the distinction of which is the use of the image background. This allows to simplify the process of scaling and segmentation (since all the pixels in the background are represented by the same color), non-uniform brightness of the face, and not to use the threshold settings and additional parameters.

A binary image scaling method is proposed, the distinction of which is the use of an arithmetic mean filter with threshold processing and fast wavelet transform. This allows to speed up the image segmentation process by about P2 times, where P is the scaling parameter, and not to use the time-consuming procedure for determining.

A binary scaled image segmentation method is proposed, the distinction of which is the use of density clustering. This allows to separate areas of the face of non-uniform brightness from the image background, noise and outliers. It also allows clusters to have different shapes and sizes, to not require setting the number of clusters and additional parameters.

To determine the scaling parameter, numerous studies were conducted in this work, which concluded that the dependence of the segmentation time on the scaling parameter is close to exponential. It was also found that for small P, where P is the scaling parameter, the quality of face detection deteriorates slightly.

The proposed technique for face detection in image based on binarization, scaling and segmentation can be used in intelligent computer systems for biometric identification of a person by the face image

Author Biographies

Eugene Fedorov, Cherkasy State Technological University Shevchenka blvd., 460, Cherkasy, Ukraine, 18006

Doctor of Technical Sciences, Associate Professor

Department of Robotics and Specialized Computer Systems

Tetyana Utkina, Cherkasy State Technological University Shevchenka blvd., 460, Cherkasy, Ukraine, 18006

PhD, Associate Professor

Department of Robotics and Specialized Computer Systems

Olga Nechyporenko, Cherkasy State Technological University Shevchenka blvd., 460, Cherkasy, Ukraine, 18006

PhD, Associate Professor

Department of Robotics and Specialized Computer Systems

Yaroslav Korpan, Cherkasy State Technological University Shevchenka blvd., 460, Cherkasy, Ukraine, 18006

PhD, Associate Professor

Department of Robotics and Specialized Computer Systems

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Published

2020-02-29

How to Cite

Fedorov, E., Utkina, T., Nechyporenko, O., & Korpan, Y. (2020). Development of technique for face detection in image based on binarization, scaling and segmentation methods. Eastern-European Journal of Enterprise Technologies, 1(9 (103), 23–31. https://doi.org/10.15587/1729-4061.2020.195369

Issue

Vol. 1 No. 9 (103) (2020): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2020 Eugene Fedorov, Tetyana Utkina, Olga Nechyporenko, Yaroslav Korpan

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